importance of scientific method in our daily life essay

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A Guide to Using the Scientific Method in Everyday Life

The  scientific method —the process used by scientists to understand the natural world—has the merit of investigating natural phenomena in a rigorous manner. Working from hypotheses, scientists draw conclusions based on empirical data. These data are validated on large-scale numbers and take into consideration the intrinsic variability of the real world. For people unfamiliar with its intrinsic jargon and formalities, science may seem esoteric. And this is a huge problem: science invites criticism because it is not easily understood. So why is it important, then, that every person understand how science is done?

Because the scientific method is, first of all, a matter of logical reasoning and only afterwards, a procedure to be applied in a laboratory.

Individuals without training in logical reasoning are more easily victims of distorted perspectives about themselves and the world. An example is represented by the so-called “ cognitive biases ”—systematic mistakes that individuals make when they try to think rationally, and which lead to erroneous or inaccurate conclusions. People can easily  overestimate the relevance  of their own behaviors and choices. They can  lack the ability to self-estimate the quality of their performances and thoughts . Unconsciously, they could even end up selecting only the arguments  that support their hypothesis or beliefs . This is why the scientific framework should be conceived not only as a mechanism for understanding the natural world, but also as a framework for engaging in logical reasoning and discussion.

A brief history of the scientific method

The scientific method has its roots in the sixteenth and seventeenth centuries. Philosophers Francis Bacon and René Descartes are often credited with formalizing the scientific method because they contrasted the idea that research should be guided by metaphysical pre-conceived concepts of the nature of reality—a position that, at the time,  was highly supported by their colleagues . In essence, Bacon thought that  inductive reasoning based on empirical observation was critical to the formulation of hypotheses  and the  generation of new understanding : general or universal principles describing how nature works are derived only from observations of recurring phenomena and data recorded from them. The inductive method was used, for example, by the scientist Rudolf Virchow to formulate the third principle of the notorious  cell theory , according to which every cell derives from a pre-existing one. The rationale behind this conclusion is that because all observations of cell behavior show that cells are only derived from other cells, this assertion must be always true. 

Inductive reasoning, however, is not immune to mistakes and limitations. Referring back to cell theory, there may be rare occasions in which a cell does not arise from a pre-existing one, even though we haven’t observed it yet—our observations on cell behavior, although numerous, can still benefit from additional observations to either refute or support the conclusion that all cells arise from pre-existing ones. And this is where limited observations can lead to erroneous conclusions reasoned inductively. In another example, if one never has seen a swan that is not white, they might conclude that all swans are white, even when we know that black swans do exist, however rare they may be.  

The universally accepted scientific method, as it is used in science laboratories today, is grounded in  hypothetico-deductive reasoning . Research progresses via iterative empirical testing of formulated, testable hypotheses (formulated through inductive reasoning). A testable hypothesis is one that can be rejected (falsified) by empirical observations, a concept known as the  principle of falsification . Initially, ideas and conjectures are formulated. Experiments are then performed to test them. If the body of evidence fails to reject the hypothesis, the hypothesis stands. It stands however until and unless another (even singular) empirical observation falsifies it. However, just as with inductive reasoning, hypothetico-deductive reasoning is not immune to pitfalls—assumptions built into hypotheses can be shown to be false, thereby nullifying previously unrejected hypotheses. The bottom line is that science does not work to prove anything about the natural world. Instead, it builds hypotheses that explain the natural world and then attempts to find the hole in the reasoning (i.e., it works to disprove things about the natural world).

How do scientists test hypotheses?

Controlled experiments

The word “experiment” can be misleading because it implies a lack of control over the process. Therefore, it is important to understand that science uses controlled experiments in order to test hypotheses and contribute new knowledge. So what exactly is a controlled experiment, then? 

Let us take a practical example. Our starting hypothesis is the following: we have a novel drug that we think inhibits the division of cells, meaning that it prevents one cell from dividing into two cells (recall the description of cell theory above). To test this hypothesis, we could treat some cells with the drug on a plate that contains nutrients and fuel required for their survival and division (a standard cell biology assay). If the drug works as expected, the cells should stop dividing. This type of drug might be useful, for example, in treating cancers because slowing or stopping the division of cells would result in the slowing or stopping of tumor growth.

Although this experiment is relatively easy to do, the mere process of doing science means that several experimental variables (like temperature of the cells or drug, dosage, and so on) could play a major role in the experiment. This could result in a failed experiment when the drug actually does work, or it could give the appearance that the drug is working when it is not. Given that these variables cannot be eliminated, scientists always run control experiments in parallel to the real ones, so that the effects of these other variables can be determined.  Control experiments  are designed so that all variables, with the exception of the one under investigation, are kept constant. In simple terms, the conditions must be identical between the control and the actual experiment.     

Coming back to our example, when a drug is administered it is not pure. Often, it is dissolved in a solvent like water or oil. Therefore, the perfect control to the actual experiment would be to administer pure solvent (without the added drug) at the same time and with the same tools, where all other experimental variables (like temperature, as mentioned above) are the same between the two (Figure 1). Any difference in effect on cell division in the actual experiment here can be attributed to an effect of the drug because the effects of the solvent were controlled.

In order to provide evidence of the quality of a single, specific experiment, it needs to be performed multiple times in the same experimental conditions. We call these multiple experiments “replicates” of the experiment (Figure 2). The more replicates of the same experiment, the more confident the scientist can be about the conclusions of that experiment under the given conditions. However, multiple replicates under the same experimental conditions  are of no help  when scientists aim at acquiring more empirical evidence to support their hypothesis. Instead, they need  independent experiments  (Figure 3), in their own lab and in other labs across the world, to validate their results. 

Often times, especially when a given experiment has been repeated and its outcome is not fully clear, it is better  to find alternative experimental assays  to test the hypothesis. 

Applying the scientific approach to everyday life

So, what can we take from the scientific approach to apply to our everyday lives?

A few weeks ago, I had an agitated conversation with a bunch of friends concerning the following question: What is the definition of intelligence?

Defining “intelligence” is not easy. At the beginning of the conversation, everybody had a different, “personal” conception of intelligence in mind, which – tacitly – implied that the conversation could have taken several different directions. We realized rather soon that someone thought that an intelligent person is whoever is able to adapt faster to new situations; someone else thought that an intelligent person is whoever is able to deal with other people and empathize with them. Personally, I thought that an intelligent person is whoever displays high cognitive skills, especially in abstract reasoning. 

The scientific method has the merit of providing a reference system, with precise protocols and rules to follow. Remember: experiments must be reproducible, which means that an independent scientists in a different laboratory, when provided with the same equipment and protocols, should get comparable results.  Fruitful conversations as well need precise language, a kind of reference vocabulary everybody should agree upon, in order to discuss about the same “content”. This is something we often forget, something that was somehow missing at the opening of the aforementioned conversation: even among friends, we should always agree on premises, and define them in a rigorous manner, so that they are the same for everybody. When speaking about “intelligence”, we must all make sure we understand meaning and context of the vocabulary adopted in the debate (Figure 4, point 1).  This is the first step of “controlling” a conversation.

There is another downside that a discussion well-grounded in a scientific framework would avoid. The mistake is not structuring the debate so that all its elements, except for the one under investigation, are kept constant (Figure 4, point 2). This is particularly true when people aim at making comparisons between groups to support their claim. For example, they may try to define what intelligence is by comparing the  achievements in life of different individuals: “Stephen Hawking is a brilliant example of intelligence because of his great contribution to the physics of black holes”. This statement does not help to define what intelligence is, simply because it compares Stephen Hawking, a famous and exceptional physicist, to any other person, who statistically speaking, knows nothing about physics. Hawking first went to the University of Oxford, then he moved to the University of Cambridge. He was in contact with the most influential physicists on Earth. Other people were not. All of this, of course, does not disprove Hawking’s intelligence; but from a logical and methodological point of view, given the multitude of variables included in this comparison, it cannot prove it. Thus, the sentence “Stephen Hawking is a brilliant example of intelligence because of his great contribution to the physics of black holes” is not a valid argument to describe what intelligence is. If we really intend to approximate a definition of intelligence, Steven Hawking should be compared to other physicists, even better if they were Hawking’s classmates at the time of college, and colleagues afterwards during years of academic research. 

In simple terms, as scientists do in the lab, while debating we should try to compare groups of elements that display identical, or highly similar, features. As previously mentioned, all variables – except for the one under investigation – must be kept constant.

This insightful piece  presents a detailed analysis of how and why science can help to develop critical thinking.

In a nutshell

Here is how to approach a daily conversation in a rigorous, scientific manner:

• First discuss about the reference vocabulary, then discuss about the content of the discussion.  Think about a researcher who is writing down an experimental protocol that will be used by thousands of other scientists in varying continents. If the protocol is rigorously written, all scientists using it should get comparable experimental outcomes. In science this means reproducible knowledge, in daily life this means fruitful conversations in which individuals are on the same page. 
• Adopt “controlled” arguments to support your claims.  When making comparisons between groups, visualize two blank scenarios. As you start to add details to both of them, you have two options. If your aim is to hide a specific detail, the better is to design the two scenarios in a completely different manner—it is to increase the variables. But if your intention is to help the observer to isolate a specific detail, the better is to design identical scenarios, with the exception of the intended detail—it is therefore to keep most of the variables constant. This is precisely how scientists ideate adequate experiments to isolate new pieces of knowledge, and how individuals should orchestrate their thoughts in order to test them and facilitate their comprehension to others.   

Not only the scientific method should offer individuals an elitist way to investigate reality, but also an accessible tool to properly reason and discuss about it.

Edited by Jason Organ, PhD, Indiana University School of Medicine.

Simone is a molecular biologist on the verge of obtaining a doctoral title at the University of Ulm, Germany. He is Vice-Director at Culturico (https://culturico.com/), where his writings span from Literature to Sociology, from Philosophy to Science. His writings recently appeared in Psychology Today, openDemocracy, Splice Today, Merion West, Uncommon Ground and The Society Pages. Follow Simone on Twitter: @simredaelli

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This has to be the best article I have ever read on Scientific Thinking. I am presently writing a treatise on how Scientific thinking can be adopted to entreat all situations.And how, a 4 year old child can be taught to adopt Scientific thinking, so that, the child can look at situations that bothers her and she could try to think about that situation by formulating the right questions. She may not have the tools to find right answers? But, forming questions by using right technique ? May just make her find a way to put her mind to rest even at that level. That is why, 4 year olds are often “eerily: (!)intelligent, I have iften been intimidated and plain embarrassed to see an intelligent and well spoken 4 year old deal with celibrity ! Of course, there are a lot of variables that have to be kept in mind in order to train children in such controlled thinking environment, as the screenplay of little Sheldon shows. Thanking the author with all my heart – #ershadspeak #wearescience #weareallscientists Ershad Khandker

Simone, thank you for this article. I have the idea that I want to apply what I learned in Biology to everyday life. You addressed this issue, and have given some basic steps in using the scientific method.

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What is the Scientific Method: How does it work and why is it important?

The scientific method is a systematic process involving steps like defining questions, forming hypotheses, conducting experiments, and analyzing data. It minimizes biases and enables replicable research, leading to groundbreaking discoveries like Einstein's theory of relativity, penicillin, and the structure of DNA. This ongoing approach promotes reason, evidence, and the pursuit of truth in science.

Updated on November 18, 2023

Beginning in elementary school, we are exposed to the scientific method and taught how to put it into practice. As a tool for learning, it prepares children to think logically and use reasoning when seeking answers to questions.

Rather than jumping to conclusions, the scientific method gives us a recipe for exploring the world through observation and trial and error. We use it regularly, sometimes knowingly in academics or research, and sometimes subconsciously in our daily lives.

In this article we will refresh our memories on the particulars of the scientific method, discussing where it comes from, which elements comprise it, and how it is put into practice. Then, we will consider the importance of the scientific method, who uses it and under what circumstances.

What is the scientific method?

The scientific method is a dynamic process that involves objectively investigating questions through observation and experimentation . Applicable to all scientific disciplines, this systematic approach to answering questions is more accurately described as a flexible set of principles than as a fixed series of steps.

The following representations of the scientific method illustrate how it can be both condensed into broad categories and also expanded to reveal more and more details of the process. These graphics capture the adaptability that makes this concept universally valuable as it is relevant and accessible not only across age groups and educational levels but also within various contexts.

Steps in the scientific method

While the scientific method is versatile in form and function, it encompasses a collection of principles that create a logical progression to the process of problem solving:

• Define a question : Constructing a clear and precise problem statement that identifies the main question or goal of the investigation is the first step. The wording must lend itself to experimentation by posing a question that is both testable and measurable.
• Gather information and resources : Researching the topic in question to find out what is already known and what types of related questions others are asking is the next step in this process. This background information is vital to gaining a full understanding of the subject and in determining the best design for experiments. 
• Form a hypothesis : Composing a concise statement that identifies specific variables and potential results, which can then be tested, is a crucial step that must be completed before any experimentation. An imperfection in the composition of a hypothesis can result in weaknesses to the entire design of an experiment.
• Perform the experiments : Testing the hypothesis by performing replicable experiments and collecting resultant data is another fundamental step of the scientific method. By controlling some elements of an experiment while purposely manipulating others, cause and effect relationships are established.
• Analyze the data : Interpreting the experimental process and results by recognizing trends in the data is a necessary step for comprehending its meaning and supporting the conclusions. Drawing inferences through this systematic process lends substantive evidence for either supporting or rejecting the hypothesis.
• Report the results : Sharing the outcomes of an experiment, through an essay, presentation, graphic, or journal article, is often regarded as a final step in this process. Detailing the project's design, methods, and results not only promotes transparency and replicability but also adds to the body of knowledge for future research.
• Retest the hypothesis : Repeating experiments to see if a hypothesis holds up in all cases is a step that is manifested through varying scenarios. Sometimes a researcher immediately checks their own work or replicates it at a future time, or another researcher will repeat the experiments to further test the hypothesis.

Where did the scientific method come from?

Oftentimes, ancient peoples attempted to answer questions about the unknown by:

• Making simple observations
• Discussing the possibilities with others deemed worthy of a debate
• Drawing conclusions based on dominant opinions and preexisting beliefs

For example, take Greek and Roman mythology. Myths were used to explain everything from the seasons and stars to the sun and death itself.

However, as societies began to grow through advancements in agriculture and language, ancient civilizations like Egypt and Babylonia shifted to a more rational analysis for understanding the natural world. They increasingly employed empirical methods of observation and experimentation that would one day evolve into the scientific method . 

In the 4th century, Aristotle, considered the Father of Science by many, suggested these elements , which closely resemble the contemporary scientific method, as part of his approach for conducting science:

• Study what others have written about the subject.
• Look for the general consensus about the subject.
• Perform a systematic study of everything even partially related to the topic.

By continuing to emphasize systematic observation and controlled experiments, scholars such as Al-Kindi and Ibn al-Haytham helped expand this concept throughout the Islamic Golden Age . 

In his 1620 treatise, Novum Organum , Sir Francis Bacon codified the scientific method, arguing not only that hypotheses must be tested through experiments but also that the results must be replicated to establish a truth. Coming at the height of the Scientific Revolution, this text made the scientific method accessible to European thinkers like Galileo and Isaac Newton who then put the method into practice.

As science modernized in the 19th century, the scientific method became more formalized, leading to significant breakthroughs in fields such as evolution and germ theory. Today, it continues to evolve, underpinning scientific progress in diverse areas like quantum mechanics, genetics, and artificial intelligence.

Why is the scientific method important?

The history of the scientific method illustrates how the concept developed out of a need to find objective answers to scientific questions by overcoming biases based on fear, religion, power, and cultural norms. This still holds true today.

By implementing this standardized approach to conducting experiments, the impacts of researchers’ personal opinions and preconceived notions are minimized. The organized manner of the scientific method prevents these and other mistakes while promoting the replicability and transparency necessary for solid scientific research.

The importance of the scientific method is best observed through its successes, for example: 

• “ Albert Einstein stands out among modern physicists as the scientist who not only formulated a theory of revolutionary significance but also had the genius to reflect in a conscious and technical way on the scientific method he was using.” Devising a hypothesis based on the prevailing understanding of Newtonian physics eventually led Einstein to devise the theory of general relativity .
• Howard Florey “Perhaps the most useful lesson which has come out of the work on penicillin has been the demonstration that success in this field depends on the development and coordinated use of technical methods.” After discovering a mold that prevented the growth of Staphylococcus bacteria, Dr. Alexander Flemimg designed experiments to identify and reproduce it in the lab, thus leading to the development of penicillin .
• James D. Watson “Every time you understand something, religion becomes less likely. Only with the discovery of the double helix and the ensuing genetic revolution have we had grounds for thinking that the powers held traditionally to be the exclusive property of the gods might one day be ours. . . .” By using wire models to conceive a structure for DNA, Watson and Crick crafted a hypothesis for testing combinations of amino acids, X-ray diffraction images, and the current research in atomic physics, resulting in the discovery of DNA’s double helix structure .

Final thoughts

As the cases exemplify, the scientific method is never truly completed, but rather started and restarted. It gave these researchers a structured process that was easily replicated, modified, and built upon. 

While the scientific method may “end” in one context, it never literally ends. When a hypothesis, design, methods, and experiments are revisited, the scientific method simply picks up where it left off. Each time a researcher builds upon previous knowledge, the scientific method is restored with the pieces of past efforts.

By guiding researchers towards objective results based on transparency and reproducibility, the scientific method acts as a defense against bias, superstition, and preconceived notions. As we embrace the scientific method's enduring principles, we ensure that our quest for knowledge remains firmly rooted in reason, evidence, and the pursuit of truth.

The AJE Team

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Importance of the Scientific Method

The scientific method attempts to minimize the influence of bias or prejudice in the experimenter. Even the best-intentioned scientists can't escape bias. It results from personal beliefs, as well as cultural beliefs, which means any human filters information based on his or her own experience. Unfortunately, this filtering process can cause a scientist to prefer one outcome over another. For someone trying to solve a problem around the house, succumbing to these kinds of biases is not such a big deal. But in the scientific community, where results have to be reviewed and duplicated, bias must be avoided at all costs.

­T­hat's the job of the scientific method. It provides an objective, standardized approach to conducting experiments and, in doing so, improves their results. By using a standardized approach in their investigations, scientists can feel confident that they will stick to the facts and limit the influence of personal, preconceived notions. Even with such a rigorous methodology in place, some scientists still make mistakes. For example, they can mistake a hypothesis for an explanation of a phenomenon without performing experiments. Or they can fail to accurately account for errors, such as measurement errors. Or they can ignore data that does not support the hypothesis.

Gregor Mendel (1822-1884), an Austrian priest who studied the inheritance of traits in pea plants and helped pioneer the study of genetics, may have fallen victim to a kind of error known as confirmation bias . Confirmation bias is the tendency to see data that supports a hypothesis while ignoring data that does not. Some argue that Mendel obtained a certain result using a small sample size, then continued collecting and censoring data to make sure his original result was confirmed. Although subsequent experiments have proven Mendel's hypothesis, many people still question his methods of experimentation.

Most of the time, however, the scientific method works and works well. When a hypothesis or a group of related hypotheses have been confirmed through repeated experimental tests, it may become a theory , which can be thought of as the pot of gold at the end of the scientific method rainbow.

Theories are much broader in scope than hypotheses and hold enormous predictive power. The theory of relativity, for example, predicted the existence of black holes long before there was evidence to support the idea. It should be noted, however, that one of the goals of science is not to prove theories right, but to prove them wrong. When this happens, a theory must be modified or discarded altogether.

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The Scientific Method in the Real Life

It goes without saying that the scientific method plays a highly essential role in the design and performance of experiments. In general, the scientific method may be defined as a series of specific processes that investigators may use in order to gather impartial, systematic, and well-founded knowledge and answer particular questions concerning the natural world (Wolf, 2018). It generally involves observations, the formulation of questions, hypotheses, experiment, data analysis, and conclusion. The method’s significance is determined by its ability to minimize experimental bias and errors to increase the accuracy and reliability of results. Although every scientific experiment may be regarded as the application of the scientific method, it is frequently used by non-scientists on an everyday basis as well.

Traditionally, the scientific method includes six steps – observation, question, hypothesis, experiment, results, and conclusion (Kuforiji & Kuforiji, 2016).

Observation is regarded as a primary process that is highly essential for the making of hypotheses and experiments in the future. A specific sort of phenomenon that occurs in the world should be observed by an individual who questions its occurrence and wants to receive answers. For any scientific project, observation is highly essential as it forms the main idea of the work.

When observation is completed, the next step of the scientific method requires the formulation of a specific question based on a previous process. For instance: Why is this event occurring? Why does it happen exactly this way? This question should address the outcomes of an experiment in order to accomplish it or make a discovery. Asking questions frequently happens simultaneously with observation as a confusing occurrence of any phenomenon that directly causes questions concerning its reasons.

The hypothesis may be regarded as a key component and the most essential step of the scientific process. It is a specific idea suggested as an answer to a previously posed question and a suitable explanation for a particular natural event, experience, or condition that should be tested through an experiment (Trefil & Hazen, 2016). The hypothesis should state the experiment’s purpose, variables, and predicted outcome.

In turn, the experiment is designed and conducted to test and either support or falsify the hypothesis. For the development of an accurate conclusion, a successful experiment requires the planning of its procedure with the identification of dependent and independent variables and the connection between them.

In the framework of the scientific method, results report the outcomes of the experiment. They traditionally include all observations and data that is received from the process of the experiment.

Finally, the scientific method includes the conclusion that implies the analysis of all results of the experiment to support or reject the hypothesis.

Concerning the situation with Buddy, the following facts may be highlighted:

• Buddy will stay without its owner for a few weeks;
• The dog was accidentally left without its bag;
• Buddy’s owner cannot be contacted, and there is no access to her house;
• The dog is new, and that is why no one knows its routine;
• The salesperson from the local pet store helped to choose supplies and food for the dog;
• A bag of salmon and potato dry dog food, a dog bed, and a few toys were bought for Buddy;
• Food for the dog was put in a bowl that was used to store lemons and oranges;
• Buddy refused to eat.

There is a testable hypothesis based on received data that may explain why Buddy refuses to eat: Buddy is currently not eating because I did not buy the type of dog food that it prefers.

This hypothesis was developed due to the following evidence:

• I did not ask Buddy’s owner any questions about the dog’s diet;
• I bought a bag of salmon and potato dry dog food according to the advice of the salesperson who is not aware of the dog’s routine as well.

However, there may be another testable hypothesis based on received data that may explain why Buddy refuses to eat: Buddy is currently not eating because it may miss the voice of its owner while it eats.

• Buddy belongs to my friend and lived with her for a certain period of time before her trip.
• An experiment will consist of providing the dog with another type of dry food with different tastes. If Buddy eats, it will mean that its initial rejection to eat was connected with an inappropriate food type.
• For this experiment, I will buy several types of dry dog food as the dog probably does not like its taste – chicken dry dog food, beef dry dog food, and rabbit dry dog food. I will offer all types to the dog to observe its reaction.

In this experiment, different types of dog food may be defined as independent variables, while the dog’s reactions to food and its eating or refusal are dependent variables. Moreover, in order to test the hypothesis that Buddy does not eat due to an inappropriate food type, other things should remain constant. That is why I should give food in the same plastic bowl, at the same time of the day, and by myself to be sure that the dog’s behavior is not connected with the bowl’s probable smell, a different routine, and my personality, voice, or smell. If Buddy eats another type of dry dog food with another taste, my hypothesis is supported, however, if it rejects all types of dog food, my hypothesis is unsustainable.

During the experiment, the initial hypothesis may be discovered unsuitable and Buddy will refuse to eat all types of dry dog food. In this case, the induction of a new testable hypothesis should be made within the cyclic framework of the scientific method. The new hypothesis should be tested and verified through systematic observation. The cyclic application of the method should be repeated with various hypotheses until a suitable hypothesis will be proven by experiments.

That is why I may test another hypothesis – Buddy is currently not eating because it may miss the voice of its owner while it eats – by playing a recording of its owner’s voice while it eats. If Buddy starts to eat, a new hypothesis will be proven. If it is shown to be unsuitable as well, I may test one more hypothesis – Buddy does not eat because it does not like the smell of a plastic bowl – by changing this bowl.

If I have a chance to talk with my friend, I will definitely ask her the following questions in order to support or disprove my hypothesis:

• What kind of food does Buddy like?

This question may be regarded as the most essential one. If my friend answers that her dog has a specific diet or prefers a particular type of dog food, her response will definitely support my hypothesis concerning the connection of the dog’s behavior with inappropriate food. However, if Buddy is not fastidious about its food, there may be another reason for its rejection to eat.

• What is its reaction to the smell of oranges and lemons?

This question will help to understand if the dog’s behavior is connected with a plastic bowl that was used to store oranges and lemons. The smell of citruses may be unpleasant for a dog and it will not eat from this bowl.

• When does Buddy usually eat?

The dog may have a particular routine and is not hungry when I try to feed it.

My neighbor suggests that Buddy refuses to eat due to its emotions related to its owner. It feels that its owner may be unlucky during her trip. This hypothesis cannot be tested as the ability of dogs to feel particular future events is not scientifically proved. In addition, we cannot test whether Buddy has emotions, such as fear or worry connected with its owner. According to Kujala (2017), “dogs have many degrees of emotion, but the full extent of dog emotions remains unknown” (p. 1).

Works cited

Kuforiji, J. O., & Kuforiji, P. O. (2016). Scientific method of research in social and human sciences: The practical steps. National Social Science Journal , 49(1), 17-23.

Kujala, M. V. (2017). Canine emotions as seen through human social cognition. Animal Sentience , 14(1), 1-34.

Trefil, J., & Hazen, R. M. (2016). The sciences: An integrated approach (8th ed.). Hoboken, NJ: Wiley.

Wolf, A. (2018). Essentials of scientific method . New York, NY: Routledge.

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Biology library

Course: biology library   >   unit 1, the scientific method.

• Controlled experiments
• The scientific method and experimental design

Introduction

• Make an observation.
• Ask a question.
• Form a hypothesis , or testable explanation.
• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

• Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

• Test of prediction: Plug the toaster into a different outlet and try again.
• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

• Iteration time!
• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
• If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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How to Use the Scientific Method in Everyday Life

How to Set Up a Controlled Science Experiment

The scientific method is a procedure consisting of a series of steps with the goal of problem-solving and information-gathering. The scientific method begins with the recognition of a problem and a clear elaboration or description of the problem itself. A process of experimentation and data collection then follows. The final steps consist of the formulation and testing of a hypothesis or potential solution and conclusion. For people unaccustomed to using the scientific method, the process may seem abstract and unapproachable. With a little consideration and observation, any problem encountered in daily life is a potential possibility to use the scientific method.

Locate or identify a problem to solve. Your personal environment is a good place to start, either in the workplace, the home, or your town or city.

Describe the problem in detail. Make quantifiable observations, such as number of times of occurrence, duration, specific physical measurements, and so on.

Form a hypothesis about what the possible cause of the problem might be, or what a potential solution could be. Check if the previously collected data suggests a pattern or possible cause.

Test your hypothesis either through further observation of the problem or by creating an experiment that highlights the aspect of the problem you wish to test. For example, if you suspect a faulty wire is the cause of a light not working, you must find a way to isolate and test whether or not the wire is actually the cause.

Repeat the steps of observation, hypothesis formation and testing until you reach a conclusion that is reinforced by supporting data or directly solves the problem at hand.

• The scientific method is best suited to solving problems without direct or simple answers. For example, a light bulb that burns out may simply need to be replaced. A light bulb that works intermittently is a much more suitable candidate for use of the scientific method, because of all of the potential causes of it not working.

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• Britannica Online Encyclopedia: Scientific Method; June 2011

About the Author

Alex Jakubik began his writing career in 2000 with book-cover summaries for Barnes & Noble. He has also authored concert programs and travel blogs, and worked both nationally and internationally in the arts. Jakubik holds a Bachelor of Music degree from Indiana University and a Master of Music from Yale University.

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Understanding Science

How science REALLY works...

• Understanding Science 101
• Misconceptions

Science affects our everyday lives in many ways.

Misconception:  Science isn’t important in my life.

Correction:  Science is deeply interwoven with our everyday lives.  Read more about it.

What has science done for you lately?

Plenty. If you think  science  doesn’t matter much to you, think again. Science affects us all, every day of the year, from the moment we wake up, all day long, and through the night. Your digital alarm clock, the weather report, the asphalt you drive on, the bus you ride in, your decision to eat a baked potato instead of fries, your cell phone, the antibiotics that treat your sore throat, the clean water that comes from your faucet, and the light that you turn off at the end of the day have all been brought to you courtesy of science. The modern world would not be modern at all without the understandings and  technology  enabled by science.

To make it clear how deeply science is interwoven with our lives, just try imagining a day without scientific progress. Just for starters, without modern science, there would be:

• no plastic.  The first completely synthetic plastic was made by a chemist in the early 1900s, and since then, chemistry has developed a wide variety of plastics suited for all sorts of jobs, from blocking bullets to making slicker dental floss.
• no modern agriculture.  Science has transformed the way we eat today. In the 1940s, biologists began developing high-yield varieties of corn, wheat, and rice, which, when paired with new fertilizers and pesticides developed by chemists, dramatically increased the amount of food that could be harvested from a single field, ushering in the Green Revolution. These science-based technologies triggered striking changes in agriculture, massively increasing the amount of food available to feed the world and simultaneously transforming the economic structure of agricultural practices.
• no modern medicine.  In the late 1700s, Edward Jenner first convincingly showed that vaccination worked. In the 1800s, scientists and doctors established the  theory  that many diseases are caused by germs. And in the 1920s, a biologist discovered the first antibiotic. From the eradication of smallpox, to the prevention of nutritional deficiencies, to successful treatments for once deadly infections, the impact of modern medicine on global health has been powerful. In fact, without science, many people alive today would have instead died of diseases that are now easily treated.

Scientific knowledge can improve the quality of life at many different levels — from the routine workings of our everyday lives to global issues. Science informs public policy and personal decisions on energy, conservation, agriculture, health, transportation, communication, defense, economics, leisure, and exploration. It’s almost impossible to overstate how many aspects of modern life are impacted by scientific knowledge. Here we’ll discuss just a few of these examples. You can investigate:

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Perspective: Dimensions of the scientific method

Eberhard o. voit.

Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America

The scientific method has been guiding biological research for a long time. It not only prescribes the order and types of activities that give a scientific study validity and a stamp of approval but also has substantially shaped how we collectively think about the endeavor of investigating nature. The advent of high-throughput data generation, data mining, and advanced computational modeling has thrown the formerly undisputed, monolithic status of the scientific method into turmoil. On the one hand, the new approaches are clearly successful and expect the same acceptance as the traditional methods, but on the other hand, they replace much of the hypothesis-driven reasoning with inductive argumentation, which philosophers of science consider problematic. Intrigued by the enormous wealth of data and the power of machine learning, some scientists have even argued that significant correlations within datasets could make the entire quest for causation obsolete. Many of these issues have been passionately debated during the past two decades, often with scant agreement. It is proffered here that hypothesis-driven, data-mining–inspired, and “allochthonous” knowledge acquisition, based on mathematical and computational models, are vectors spanning a 3D space of an expanded scientific method. The combination of methods within this space will most certainly shape our thinking about nature, with implications for experimental design, peer review and funding, sharing of result, education, medical diagnostics, and even questions of litigation.

The traditional scientific method: Hypothesis-driven deduction

Research is the undisputed core activity defining science. Without research, the advancement of scientific knowledge would come to a screeching halt. While it is evident that researchers look for new information or insights, the term “research” is somewhat puzzling. Never mind the prefix “re,” which simply means “coming back and doing it again and again,” the word “search” seems to suggest that the research process is somewhat haphazard, that not much of a strategy is involved in the process. One might argue that research a few hundred years ago had the character of hoping for enough luck to find something new. The alchemists come to mind in their quest to turn mercury or lead into gold, or to discover an elixir for eternal youth, through methods we nowadays consider laughable.

Today’s sciences, in stark contrast, are clearly different. Yes, we still try to find something new—and may need a good dose of luck—but the process is anything but unstructured. In fact, it is prescribed in such rigor that it has been given the widely known moniker “scientific method.” This scientific method has deep roots going back to Aristotle and Herophilus (approximately 300 BC), Avicenna and Alhazen (approximately 1,000 AD), Grosseteste and Robert Bacon (approximately 1,250 AD), and many others, but solidified and crystallized into the gold standard of quality research during the 17th and 18th centuries [ 1 – 7 ]. In particular, Sir Francis Bacon (1561–1626) and René Descartes (1596–1650) are often considered the founders of the scientific method, because they insisted on careful, systematic observations of high quality, rather than metaphysical speculations that were en vogue among the scholars of the time [ 1 , 8 ]. In contrast to their peers, they strove for objectivity and insisted that observations, rather than an investigator’s preconceived ideas or superstitions, should be the basis for formulating a research idea [ 7 , 9 ].

Bacon and his 19th century follower John Stuart Mill explicitly proposed gaining knowledge through inductive reasoning: Based on carefully recorded observations, or from data obtained in a well-planned experiment, generalized assertions were to be made about similar yet (so far) unobserved phenomena [ 7 ]. Expressed differently, inductive reasoning attempts to derive general principles or laws directly from empirical evidence [ 10 ]. An example is the 19th century epigram of the physician Rudolf Virchow, Omnis cellula e cellula . There is no proof that indeed “every cell derives from a cell,” but like Virchow, we have made the observation time and again and never encountered anything suggesting otherwise.

In contrast to induction, the widely accepted, traditional scientific method is based on formulating and testing hypotheses. From the results of these tests, a deduction is made whether the hypothesis is presumably true or false. This type of hypotheticodeductive reasoning goes back to William Whewell, William Stanley Jevons, and Charles Peirce in the 19th century [ 1 ]. By the 20th century, the deductive, hypothesis-based scientific method had become deeply ingrained in the scientific psyche, and it is now taught as early as middle school in order to teach students valid means of discovery [ 8 , 11 , 12 ]. The scientific method has not only guided most research studies but also fundamentally influenced how we think about the process of scientific discovery.

Alas, because biology has almost no general laws, deduction in the strictest sense is difficult. It may therefore be preferable to use the term abduction, which refers to the logical inference toward the most plausible explanation, given a set of observations, although this explanation cannot be proven and is not necessarily true.

Over the decades, the hypothesis-based scientific method did experience variations here and there, but its conceptual scaffold remained essentially unchanged ( Fig 1 ). Its key is a process that begins with the formulation of a hypothesis that is to be rigorously tested, either in the wet lab or computationally; nonadherence to this principle is seen as lacking rigor and can lead to irreproducible results [ 1 , 13 – 15 ].

The central concept of the traditional scientific method is a falsifiable hypothesis regarding some phenomenon of interest. This hypothesis is to be tested experimentally or computationally. The test results support or refute the hypothesis, triggering a new round of hypothesis formulation and testing.

Going further, the prominent philosopher of science Sir Karl Popper argued that a scientific hypothesis can never be verified but that it can be disproved by a single counterexample. He therefore demanded that scientific hypotheses had to be falsifiable, because otherwise, testing would be moot [ 16 , 17 ] (see also [ 18 ]). As Gillies put it, “successful theories are those that survive elimination through falsification” [ 19 ]. Kelley and Scott agreed to some degree but warned that complete insistence on falsifiability is too restrictive as it would mark many computational techniques, statistical hypothesis testing, and even Darwin’s theory of evolution as nonscientific [ 20 ].

While the hypothesis-based scientific method has been very successful, its exclusive reliance on deductive reasoning is dangerous because according to the so-called Duhem–Quine thesis, hypothesis testing always involves an unknown number of explicit or implicit assumptions, some of which may steer the researcher away from hypotheses that seem implausible, although they are, in fact, true [ 21 ]. According to Kuhn, this bias can obstruct the recognition of paradigm shifts [ 22 ], which require the rethinking of previously accepted “truths” and the development of radically new ideas [ 23 , 24 ]. The testing of simultaneous alternative hypotheses [ 25 – 27 ] ameliorates this problem to some degree but not entirely.

The traditional scientific method is often presented in discrete steps, but it should really be seen as a form of critical thinking, subject to review and independent validation [ 8 ]. It has proven very influential, not only by prescribing valid experimentation, but also for affecting the way we attempt to understand nature [ 18 ], for teaching [ 8 , 12 ], reporting, publishing, and otherwise sharing information [ 28 ], for peer review and the awarding of funds by research-supporting agencies [ 29 , 30 ], for medical diagnostics [ 7 ], and even in litigation [ 31 ].

A second dimension of the scientific method: Data-mining–inspired induction

A major shift in biological experimentation occurred with the–omics revolution of the early 21st century. All of a sudden, it became feasible to perform high-throughput experiments that generated thousands of measurements, typically characterizing the expression or abundances of very many—if not all—genes, proteins, metabolites, or other biological quantities in a sample.

The strategy of measuring large numbers of items in a nontargeted fashion is fundamentally different from the traditional scientific method and constitutes a new, second dimension of the scientific method. Instead of hypothesizing and testing whether gene X is up-regulated under some altered condition, the leading question becomes which of the thousands of genes in a sample are up- or down-regulated. This shift in focus elevates the data to the supreme role of revealing novel insights by themselves ( Fig 2 ). As an important, generic advantage over the traditional strategy, this second dimension is free of a researcher’s preconceived notions regarding the molecular mechanisms governing the phenomenon of interest, which are otherwise the key to formulating a hypothesis. The prominent biologists Patrick Brown and David Botstein commented that “the patterns of expression will often suffice to begin de novo discovery of potential gene functions” [ 32 ].

Data-driven research begins with an untargeted exploration, in which the data speak for themselves. Machine learning extracts patterns from the data, which suggest hypotheses that are to be tested in the lab or computationally.

This data-driven, discovery-generating approach is at once appealing and challenging. On the one hand, very many data are explored simultaneously and essentially without bias. On the other hand, the large datasets supporting this approach create a genuine challenge to understanding and interpreting the experimental results because the thousands of data points, often superimposed with a fair amount of noise, make it difficult to detect meaningful differences between sample and control. This situation can only be addressed with computational methods that first “clean” the data, for instance, through the statistically valid removal of outliers, and then use machine learning to identify statistically significant, distinguishing molecular profiles or signatures. In favorable cases, such signatures point to specific biological pathways, whereas other signatures defy direct explanation but may become the launch pad for follow-up investigations [ 33 ].

Today’s scientists are very familiar with this discovery-driven exploration of “what’s out there” and might consider it a quaint quirk of history that this strategy was at first widely chastised and ridiculed as a “fishing expedition” [ 30 , 34 ]. Strict traditionalists were outraged that rigor was leaving science with the new approach and that sufficient guidelines were unavailable to assure the validity and reproducibility of results [ 10 , 35 , 36 ].

From the view point of philosophy of science, this second dimension of the scientific method uses inductive reasoning and reflects Bacon’s idea that observations can and should dictate the research question to be investigated [ 1 , 7 ]. Allen [ 36 ] forcefully rejected this type of reasoning, stating “the thinking goes, we can now expect computer programs to derive significance, relevance and meaning from chunks of information, be they nucleotide sequences or gene expression profiles… In contrast with this view, many are convinced that no purely logical process can turn observation into understanding.” His conviction goes back to the 18th century philosopher David Hume and again to Popper, who identified as the overriding problem with inductive reasoning that it can never truly reveal causality, even if a phenomenon is observed time and again [ 16 , 17 , 37 , 38 ]. No number of observations, even if they always have the same result, can guard against an exception that would violate the generality of a law inferred from these observations [ 1 , 35 ]. Worse, Popper argued, through inference by induction, we cannot even know the probability of something being true [ 10 , 17 , 36 ].

Others argued that data-driven and hypothesis-driven research actually do not differ all that much in principle, as long as there is cycling between developing new ideas and testing them with care [ 27 ]. In fact, Kell and Oliver [ 34 ] maintained that the exclusive acceptance of hypothesis-driven programs misrepresents the complexities of biological knowledge generation. Similarly refuting the prominent rule of deduction, Platt [ 26 ] and Beard and Kushmerick [ 27 ] argued that repeated inductive reasoning, called strong inference, corresponds to a logically sound decision tree of disproving or refining hypotheses that can rapidly yield firm conclusions; nonetheless, Platt had to admit that inductive inference is not as certain as deduction, because it projects into the unknown. Lander compared the task of obtaining causality by induction to the problem of inferring the design of a microprocessor from input-output readings, which in a strict sense is impossible, because the microprocessor could be arbitrarily complicated; even so, inference often leads to novel insights and therefore is valuable [ 39 ].

An interesting special case of almost pure inductive reasoning is epidemiology, where hypothesis-driven reasoning is rare and instead, the fundamental question is whether data-based evidence is sufficient to associate health risks with specific causes [ 31 , 34 ].

Recent advances in machine learning and “big-data” mining have driven the use of inductive reasoning to unprecedented heights. As an example, machine learning can greatly assist in the discovery of patterns, for instance, in biological sequences [ 40 ]. Going a step further, a pithy article by Andersen [ 41 ] proffered that we may not need to look for causality or mechanistic explanations anymore if we just have enough correlation: “With enough data, the numbers speak for themselves, correlation replaces causation, and science can advance even without coherent models or unified theories.”

Of course, the proposal to abandon the quest for causality caused pushback on philosophical as well as mathematical grounds. Allen [ 10 , 35 ] considered the idea “absurd” that data analysis could enhance understanding in the absence of a hypothesis. He felt confident “that even the formidable combination of computing power with ease of access to data cannot produce a qualitative shift in the way that we do science: the making of hypotheses remains an indispensable component in the growth of knowledge” [ 36 ]. Succi and Coveney [ 42 ] refuted the “most extravagant claims” of big-data proponents very differently, namely by analyzing the theories on which machine learning is founded. They contrasted the assumptions underlying these theories, such as the law of large numbers, with the mathematical reality of complex biological systems. Specifically, they carefully identified genuine features of these systems, such as nonlinearities, nonlocality of effects, fractal aspects, and high dimensionality, and argued that they fundamentally violate some of the statistical assumptions implicitly underlying big-data analysis, like independence of events. They concluded that these discrepancies “may lead to false expectations and, at their nadir, even to dangerous social, economical and political manipulation.” To ameliorate the situation, the field of big-data analysis would need new strong theorems characterizing the validity of its methods and the numbers of data required for obtaining reliable insights. Succi and Coveney go as far as stating that too many data are just as bad as insufficient data [ 42 ].

While philosophical doubts regarding inductive methods will always persist, one cannot deny that -omics-based, high-throughput studies, combined with machine learning and big-data analysis, have been very successful [ 43 ]. Yes, induction cannot truly reveal general laws, no matter how large the datasets, but they do provide insights that are very different from what science had offered before and may at least suggest novel patterns, trends, or principles. As a case in point, if many transcriptomic studies indicate that a particular gene set is involved in certain classes of phenomena, there is probably some truth to the observation, even though it is not mathematically provable. Kepler’s laws of astronomy were arguably derived solely from inductive reasoning [ 34 ].

Notwithstanding the opposing views on inductive methods, successful strategies shape how we think about science. Thus, to take advantage of all experimental options while ensuring quality of research, we must not allow that “anything goes” but instead identify and characterize standard operating procedures and controls that render this emerging scientific method valid and reproducible. A laudable step in this direction was the wide acceptance of “minimum information about a microarray experiment” (MIAME) standards for microarray experiments [ 44 ].

A third dimension of the scientific method: Allochthonous reasoning

Parallel to the blossoming of molecular biology and the rapid rise in the power and availability of computing in the late 20th century, the use of mathematical and computational models became increasingly recognized as relevant and beneficial for understanding biological phenomena. Indeed, mathematical models eventually achieved cornerstone status in the new field of computational systems biology.

Mathematical modeling has been used as a tool of biological analysis for a long time [ 27 , 45 – 48 ]. Interesting for the discussion here is that the use of mathematical and computational modeling in biology follows a scientific approach that is distinctly different from the traditional and the data-driven methods, because it is distributed over two entirely separate domains of knowledge. One consists of the biological reality of DNA, elephants, and roses, whereas the other is the world of mathematics, which is governed by numbers, symbols, theorems, and abstract work protocols. Because the ways of thinking—and even the languages—are different in these two realms, I suggest calling this type of knowledge acquisition “allochthonous” (literally Greek: in or from a “piece of land different from where one is at home”; one could perhaps translate it into modern lingo as “outside one’s comfort zone”). De facto, most allochthonous reasoning in biology presently refers to mathematics and computing, but one might also consider, for instance, the application of methods from linguistics in the analysis of DNA sequences or proteins [ 49 ].

One could argue that biologists have employed “models” for a long time, for instance, in the form of “model organisms,” cell lines, or in vitro experiments, which more or less faithfully reflect features of the organisms of true interest but are easier to manipulate. However, this type of biological model use is rather different from allochthonous reasoning, as it does not leave the realm of biology and uses the same language and often similar methodologies.

A brief discussion of three experiences from our lab may illustrate the benefits of allochthonous reasoning. (1) In a case study of renal cell carcinoma, a dynamic model was able to explain an observed yet nonintuitive metabolic profile in terms of the enzymatic reaction steps that had been altered during the disease [ 50 ]. (2) A transcriptome analysis had identified several genes as displaying significantly different expression patterns during malaria infection in comparison to the state of health. Considered by themselves and focusing solely on genes coding for specific enzymes of purine metabolism, the findings showed patterns that did not make sense. However, integrating the changes in a dynamic model revealed that purine metabolism globally shifted, in response to malaria, from guanine compounds to adenine, inosine, and hypoxanthine [ 51 ]. (3) Data capturing the dynamics of malaria parasites suggested growth rates that were biologically impossible. Speculation regarding possible explanations led to the hypothesis that many parasite-harboring red blood cells might “hide” from circulation and therewith from detection in the blood stream. While experimental testing of the feasibility of the hypothesis would have been expensive, a dynamic model confirmed that such a concealment mechanism could indeed quantitatively explain the apparently very high growth rates [ 52 ]. In all three cases, the insights gained inductively from computational modeling would have been difficult to obtain purely with experimental laboratory methods. Purely deductive allochthonous reasoning is the ultimate goal of the search for design and operating principles [ 53 – 55 ], which strives to explain why certain structures or functions are employed by nature time and again. An example is a linear metabolic pathway, in which feedback inhibition is essentially always exerted on the first step [ 56 , 57 ]. This generality allows the deduction that a so far unstudied linear pathway is most likely (or even certain to be) inhibited at the first step. Not strictly deductive—but rather abductive—was a study in our lab in which we analyzed time series data with a mathematical model that allowed us to infer the most likely regulatory structure of a metabolic pathway [ 58 , 59 ].

A typical allochthonous investigation begins in the realm of biology with the formulation of a hypothesis ( Fig 3 ). Instead of testing this hypothesis with laboratory experiments, the system encompassing the hypothesis is moved into the realm of mathematics. This move requires two sets of ingredients. One set consists of the simplification and abstraction of the biological system: Any distracting details that seem unrelated to the hypothesis and its context are omitted or represented collectively with other details. This simplification step carries the greatest risk of the entire modeling approach, as omission of seemingly negligible but, in truth, important details can easily lead to wrong results. The second set of ingredients consists of correspondence rules that translate every biological component or process into the language of mathematics [ 60 , 61 ].

This mathematical and computational approach is distributed over two realms, which are connected by correspondence rules.

Once the system is translated, it has become an entirely mathematical construct that can be analyzed purely with mathematical and computational means. The results of this analysis are also strictly mathematical. They typically consist of values of variables, magnitudes of processes, sensitivity patterns, signs of eigenvalues, or qualitative features like the onset of oscillations or the potential for limit cycles. Correspondence rules are used again to move these results back into the realm of biology. As an example, the mathematical result that “two eigenvalues have positive real parts” does not make much sense to many biologists, whereas the interpretation that “the system is not stable at the steady state in question” is readily explained. New biological insights may lead to new hypotheses, which are tested either by experiments or by returning once more to the realm of mathematics. The model design, diagnosis, refinements, and validation consist of several phases, which have been discussed widely in the biomathematical literature. Importantly, each iteration of a typical modeling analysis consists of a move from the biological to the mathematical realm and back.

The reasoning within the realm of mathematics is often deductive, in the form of an Aristotelian syllogism, such as the well-known “All men are mortal; Socrates is a man; therefore, Socrates is mortal.” However, the reasoning may also be inductive, as it is the case with large-scale Monte-Carlo simulations that generate arbitrarily many “observations,” although they cannot reveal universal principles or theorems. An example is a simulation randomly drawing numbers in an attempt to show that every real number has an inverse. The simulation will always attest to this hypothesis but fail to discover the truth because it will never randomly draw 0. Generically, computational models may be considered sets of hypotheses, formulated as equations or as algorithms that reflect our perception of a complex system [ 27 ].

Impact of the multidimensional scientific method on learning

Almost all we know in biology has come from observation, experimentation, and interpretation. The traditional scientific method not only offered clear guidance for this knowledge gathering, but it also fundamentally shaped the way we think about the exploration of nature. When presented with a new research question, scientists were trained to think immediately in terms of hypotheses and alternatives, pondering the best feasible ways of testing them, and designing in their minds strong controls that would limit the effects of known or unknown confounders. Shaped by the rigidity of this ever-repeating process, our thinking became trained to move forward one well-planned step at a time. This modus operandi was rigid and exact. It also minimized the erroneous pursuit of long speculative lines of thought, because every step required testing before a new hypothesis was formed. While effective, the process was also very slow and driven by ingenuity—as well as bias—on the scientist’s part. This bias was sometimes a hindrance to necessary paradigm shifts [ 22 ].

High-throughput data generation, big-data analysis, and mathematical-computational modeling changed all that within a few decades. In particular, the acceptance of inductive principles and of the allochthonous use of nonbiological strategies to answer biological questions created an unprecedented mix of successes and chaos. To the horror of traditionalists, the importance of hypotheses became minimized, and the suggestion spread that the data would speak for themselves [ 36 ]. Importantly, within this fog of “anything goes,” the fundamental question arose how to determine whether an experiment was valid.

Because agreed-upon operating procedures affect research progress and interpretation, thinking, teaching, and sharing of results, this question requires a deconvolution of scientific strategies. Here I proffer that the single scientific method of the past should be expanded toward a vector space of scientific methods, with spanning vectors that correspond to different dimensions of the scientific method ( Fig 4 ).

The traditional hypothesis-based deductive scientific method is expanded into a 3D space that allows for synergistic blends of methods that include data-mining–inspired, inductive knowledge acquisition, and mathematical model-based, allochthonous reasoning.

Obviously, all three dimensions have their advantages and drawbacks. The traditional, hypothesis-driven deductive method is philosophically “clean,” except that it is confounded by preconceptions and assumptions. The data-mining–inspired inductive method cannot offer universal truths but helps us explore very large spaces of factors that contribute to a phenomenon. Allochthonous, model-based reasoning can be performed mentally, with paper and pencil, through rigorous analysis, or with a host of computational methods that are precise and disprovable [ 27 ]. At the same time, they are incomparable faster, cheaper, and much more comprehensive than experiments in molecular biology. This reduction in cost and time, and the increase in coverage, may eventually have far-reaching consequences, as we can already fathom from much of modern physics.

Due to its long history, the traditional dimension of the scientific method is supported by clear and very strong standard operating procedures. Similarly, strong procedures need to be developed for the other two dimensions. The MIAME rules for microarray analysis provide an excellent example [ 44 ]. On the mathematical modeling front, no such rules are generally accepted yet, but trends toward them seem to emerge at the horizon. For instance, it seems to be becoming common practice to include sensitivity analyses in typical modeling studies and to assess the identifiability or sloppiness of ensembles of parameter combinations that fit a given dataset well [ 62 , 63 ].

From a philosophical point of view, it seems unlikely that objections against inductive reasoning will disappear. However, instead of pitting hypothesis-based deductive reasoning against inductivism, it seems more beneficial to determine how the different methods can be synergistically blended ( cf . [ 18 , 27 , 34 , 42 ]) as linear combinations of the three vectors of knowledge acquisition ( Fig 4 ). It is at this point unclear to what degree the identified three dimensions are truly independent of each other, whether additional dimensions should be added [ 24 ], or whether the different versions could be amalgamated into a single scientific method [ 18 ], especially if it is loosely defined as a form of critical thinking [ 8 ]. Nobel Laureate Percy Bridgman even concluded that “science is what scientists do, and there are as many scientific methods as there are individual scientists” [ 8 , 64 ].

Combinations of the three spanning vectors of the scientific method have been emerging for some time. Many biologists already use inductive high-throughput methods to develop specific hypotheses that are subsequently tested with deductive or further inductive methods [ 34 , 65 ]. In terms of including mathematical modeling, physics and geology have been leading the way for a long time, often by beginning an investigation in theory, before any actual experiment is performed. It will benefit biology to look into this strategy and to develop best practices of allochthonous reasoning.

The blending of methods may take quite different shapes. Early on, Ideker and colleagues [ 65 ] proposed an integrated experimental approach for pathway analysis that offered a glimpse of new experimental strategies within the space of scientific methods. In a similar vein, Covert and colleagues [ 66 ] included computational methods into such an integrated approach. Additional examples of blended analyses in systems biology can be seen in other works, such as [ 43 , 67 – 73 ]. Generically, it is often beneficial to start with big data, determine patterns in associations and correlations, then switch to the mathematical realm in order to filter out spurious correlations in a high-throughput fashion. If this procedure is executed in an iterative manner, the “surviving” associations have an increased level of confidence and are good candidates for further experimental or computational testing (personal communication from S. Chandrasekaran).

If each component of a blended scientific method follows strict, commonly agreed guidelines, “linear combinations” within the 3D space can also be checked objectively, per deconvolution. In addition, guidelines for synergistic blends of component procedures should be developed. If we carefully monitor such blends, time will presumably indicate which method is best for which task and how the different approaches optimally inform each other. For instance, it will be interesting to study whether there is an optimal sequence of experiments along the three axes for a particular class of tasks. Big-data analysis together with inductive reasoning might be optimal for creating initial hypotheses and possibly refuting wrong speculations (“we had thought this gene would be involved, but apparently it isn’t”). If the logic of an emerging hypotheses can be tested with mathematical and computational tools, it will almost certainly be faster and cheaper than an immediate launch into wet-lab experimentation. It is also likely that mathematical reasoning will be able to refute some apparently feasible hypothesis and suggest amendments. Ultimately, the “surviving” hypotheses must still be tested for validity through conventional experiments. Deconvolving current practices and optimizing the combination of methods within the 3D or higher-dimensional space of scientific methods will likely result in better planning of experiments and in synergistic blends of approaches that have the potential capacity of addressing some of the grand challenges in biology.

Acknowledgments

The author is very grateful to Dr. Sriram Chandrasekaran and Ms. Carla Kumbale for superb suggestions and invaluable feedback.

Funding Statement

This work was supported in part by grants from the National Science Foundation ( https://www.nsf.gov/div/index.jsp?div=MCB ) grant NSF-MCB-1517588 (PI: EOV), NSF-MCB-1615373 (PI: Diana Downs) and the National Institute of Environmental Health Sciences ( https://www.niehs.nih.gov/ ) grant NIH-2P30ES019776-05 (PI: Carmen Marsit). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Have you got to write an essay about science in everyday life?

Every student is assigned an essay about science at some point in their academic life. 

Whether it's for a class or standardized tests, writing a science essay can seem daunting to many students.

But don't worry!

In this blog, we have gathered several essay samples that you can read. Check out these examples and get inspired to write your own essay on the topic!

Moreover, we'll give you tips on writing an essay about science in everyday life. We'll cover everything from brainstorming to editing so that you can ace that next essay with ease.

So let's get started!

• 1. Essays About Science In Everyday Life
• 2. Tips for Writing An Essay About Science

Essays About Science In Everyday Life

The following essays provide a snapshot of the different ways science can be explored in everyday life.

Each essay offers its own unique perspective on the role of science in the world around us.

Read through these essays and get a feel for the range of possibilities that are available when exploring science in your everyday life. 

So read on!

Essays About Science In Everyday Life For Students

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Blessings of Science Essay Sample

Want to read essays on scientific topics? Check out thes e science essay examples t o put your curiosity to rest.

After you've read these sample essays, try writing your own essay on a similar topic!

Continue reading to check out some tips that will help you write your essay!

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Tips for Writing An Essay About Science

You have read the sample essays and seen how they establish their point. The next step is to write an essay of your own.  

Here are some tips that will help you write a great essay about science in everyday life:

Brainstorm Ideas for a Topic

The first step in writing an essay is to choose a specific topic. Here are some questions that’ll help you brainstorm a topic. Or you can use them as prompts that you can consider for your essay:

• What are some examples of science in everyday life?
• What are some applications of science in daily life?
• Science plays an important role in modern life.
• Science is the greatest blessing for the modern man.
• How has science affected human life?
• How has modern science changed the way we live?
• How has science made life easier?
• What is the importance of science in your daily life?

In your essay, you can examine scientific discoveries that are essential for modern living. 

Topics may include telecommunications, medical breakthroughs, and other areas that impact people's lives. Check out this list of science essay topics if you need more ideas.

Here’s a video containing a list of examples of how science is involved in our daily lives. Check it out to get some ideas:

So, find an interesting topic for your essay before moving on.

Make an Essay Outline

Once you know what you will write about, start by making an essay outline . Making an essay outline is an important step for any writer. It organizes your thoughts and serves as a key reference point during the writing and editing process.

To create an effective essay outline, you should… 

• Start by thinking of a thesis statement . A thesis statement is the central idea or main point of your essay.
• Secondly, think of the main ideas or points you want to discuss. Once these are established, add supporting details, evidence, and examples for each point.
• Finally, make sure all your points have a logical flow.

An effectively planned essay outline will result in a high-quality essay! So take your time when making an outline.

Define Your Argument Clearly

When writing an essay about science in everyday life, it is important to establish the main point or argument of your essay very early on.

Your thesis statement should be expressed clearly and concisely in the introduction of your essay. 

This will set the tone for the rest of your paper and help readers understand what your essay is about.

The main points of your body paragraphs should support your main thesis. Make sure that these points are presented logically and are connected to each other. 

In short, be clear and coherent throughout your essay.

Illustrate With Examples

When writing your essay, look for examples from everyday life to illustrate your main points. 

Using specific examples will also help readers understand the importance of your argument in a practical context. 

Luckily, we live in an age of science. You will find ample inspiration for your essay around you. There are countless scientific inventions and tools you use every day, such as motor cars. 

Additionally, personal anecdotes can be especially effective in making your argument more engaging and convincing. You should also include scientific research or statistics to strengthen your argument further.

Edit Your Essay Carefully

Finally, take time to review and edit your essay. Check for grammar, punctuation, and other common errors . 

Also, make sure that your argument is logical and consistent with the evidence you provide.

Going through your essay one last time will ensure that you are satisfied with the finished product. You may also get help from an experienced essay writer to edit your essay.

To conclude,

By reading these examples and following these tips, you can easily write an essay about science in everyday life. So get started and write your best essay today!

Do you still require further help in writing your essay? 

No problem! 

At MyPerfectWords.com , we provide expert science essay writing service . We will craft an essay that is unique to your topic and tailored to your specific needs. 

So, if you're struggling to write an effective science essay, contact our essay writer service . 

Our science essay writer will help you create an outstanding essay in no time. We guarantee you'll be satisfied with the results. You can also utilize the assistance of our essay bot if you’re looking for quick essay generation.

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Science In Everyday Life: 50 Examples Showing How Science Impacts Our Daily Activities

Science plays a vital role in our daily lives, even if we don’t always realize it. From the alarm that wakes us up to the phones we scroll through before bed, advancements in science, technology, engineering, and math touch every aspect of our routines.

If you’re short on time, here’s a quick answer on examples of science in daily life: Science gives us technology like smartphones, WiFi, microwaves, and virtual assistants . It brings us medical treatments, weather forecasts, and green energy solutions.

Fields like chemistry, biology, and physics explain the world around us and advancements that enhance how we live.

This comprehensive guide provides over 50 examples demonstrating the many amazing ways science impacts our lives. We’ll cover common technologies, healthcare innovations, environmental applications, and insights science provides into the world around us.

Read on to gain appreciation for just how integral STEM is to our modern lives.

Technology Innovations from Science

Smartphones and wifi.

Smartphones have become an integral part of our lives, and we can thank science for their existence. These devices combine various technologies, such as wireless communication, touchscreen displays, and powerful processors, all made possible through scientific advancements.

With the advent of WiFi technology, we can now connect our smartphones to the internet seamlessly, allowing us to access information, communicate with others, and stay connected wherever we go. According to a report by Statista, there are over 3.8 billion smartphone users worldwide, highlighting the widespread impact of this technology.

Virtual Assistants and AI

Virtual assistants, like Siri, Alexa, and Google Assistant, have become an integral part of our daily lives. These AI-powered technologies are the result of extensive research and development in the field of artificial intelligence.

They can perform a wide range of tasks, from answering questions and setting reminders to controlling smart home devices. Virtual assistants have revolutionized the way we interact with technology and have made our lives more convenient.

According to a study by Pew Research Center, around 46% of Americans use voice assistants, showcasing the widespread adoption of this technology.

Streaming Entertainment

Gone are the days when we had to wait for our favorite TV shows or movies to air on traditional television networks. Thanks to scientific advancements, we now have streaming platforms like Netflix, Hulu, and Amazon Prime Video that allow us to enjoy a vast library of entertainment content on demand.

Streaming services rely on technologies like high-speed internet connections and video compression algorithms, which have made it possible to deliver high-quality content to our devices. According to a report by Conviva, global streaming hours increased by 57% in 2020, highlighting the growing popularity of streaming entertainment.

Kitchen Appliances

Science has also revolutionized our kitchens with innovative appliances that make cooking and food preparation easier and more efficient. From microwave ovens and induction cooktops to smart refrigerators and programmable coffee makers, these appliances utilize scientific principles to enhance our culinary experiences.

For example, microwave ovens use electromagnetic waves to heat food quickly, while induction cooktops use magnetic fields to generate heat directly in the cookware. These advancements have saved us time and energy in the kitchen, allowing us to focus on creating delicious meals.

Healthcare and Medicine

Medical treatments and drugs.

Science plays a crucial role in the development of medical treatments and drugs. Through extensive research and experimentation, scientists are able to discover new medications and therapies that help treat diseases and improve the quality of life for patients.

From antibiotics to cancer-fighting drugs, science has revolutionized the field of medicine. For instance, in recent years, breakthroughs in immunotherapy have provided hope for patients with previously untreatable cancers, offering them a chance at a longer and healthier life.

Medical Imaging and Scans

The advancement of medical imaging technology has greatly contributed to the field of healthcare. X-rays, CT scans, MRIs, and ultrasounds are all examples of medical imaging techniques that allow doctors to visualize the internal structures of the body without invasive procedures.

These imaging tools aid in the diagnosis and monitoring of various conditions, such as broken bones, tumors, and organ abnormalities. With the help of these technologies, doctors can make more accurate and timely diagnoses, leading to better treatment outcomes for patients.

Prosthetics and Implants

Science has also revolutionized the field of prosthetics and implants, providing individuals with enhanced mobility and improved quality of life. With advancements in materials science and robotics, prosthetic limbs have become increasingly sophisticated, allowing amputees to regain functionality and perform daily activities with greater ease.

Additionally, advancements in medical implants, such as pacemakers and artificial joints, have significantly improved the lives of individuals with chronic conditions, enabling them to live longer and more fulfilling lives.

Genetic Testing

Genetic testing is another area where science has had a significant impact on healthcare. With advancements in DNA sequencing technology, scientists are now able to analyze an individual’s genetic makeup and identify potential genetic disorders or predispositions to certain diseases.

This information can be used for early detection and prevention, allowing individuals to make informed decisions about their health. Genetic testing has also paved the way for personalized medicine, where treatments can be tailored to an individual’s specific genetic profile, leading to more effective and targeted therapies.

Energy and Environment

Renewable energy.

Renewable energy plays a crucial role in reducing our carbon footprint and preserving the environment. Solar power, for example, harnesses the energy from the sun and converts it into electricity, providing a sustainable and clean alternative to traditional fossil fuels.

Wind power is another example, where the kinetic energy of the wind is converted into electricity through wind turbines. According to the International Renewable Energy Agency (IRENA), renewable energy accounted for 26% of global electricity generation in 2018, and this number is expected to rise significantly in the coming years.

Harnessing the power of renewable energy sources not only reduces greenhouse gas emissions but also leads to economic growth and job creation in the renewable energy sector.

Water Filtration and Conservation

Science has greatly contributed to improving water filtration systems and promoting water conservation. Advanced technologies such as reverse osmosis and ultraviolet (UV) disinfection are used to remove impurities and pathogens from water, making it safe for consumption.

These filtration systems are essential in areas where access to clean drinking water is limited. Additionally, scientific research has led to the development of water-saving devices and techniques, such as low-flow showerheads and rainwater harvesting systems.

These innovations help conserve water resources and reduce water wastage, ultimately benefiting both the environment and our daily lives.

Weather Forecasting

Weather forecasting relies heavily on scientific advancements to accurately predict and analyze weather patterns. Meteorologists use a variety of tools and technologies, including satellites, radar systems, and computer models, to collect data and make predictions about future weather conditions.

By understanding atmospheric phenomena and analyzing historical data, scientists can provide crucial information regarding upcoming storms, hurricanes, and other weather events. Accurate weather forecasts not only help us plan our daily activities but also play a vital role in disaster preparedness and mitigation efforts, potentially saving lives and minimizing damage.

Recycling and Waste Management

In today’s world, proper waste management and recycling have become essential for the health of our environment. Science has played a significant role in developing efficient recycling processes and waste management systems.

Recycling helps reduce the amount of waste sent to landfills and conserves valuable resources. Through various scientific methods, materials such as paper, plastic, glass, and metal can be recycled and used for the production of new products.

Furthermore, advancements in waste management technologies, such as waste-to-energy systems, enable the conversion of waste materials into renewable energy sources. These innovations not only reduce the environmental impact of waste but also contribute to a more sustainable and circular economy.

Science continues to drive innovations and advancements in the energy and environmental sectors. By embracing renewable energy, implementing efficient water filtration and conservation methods, improving weather forecasting accuracy, and promoting recycling and waste management, we can create a more sustainable and environmentally friendly future.

Transportation Innovations

Aircraft technology.

Aircraft technology has come a long way since the Wright brothers’ first flight. Today, we have advanced and sophisticated airplanes that allow us to travel to any corner of the world in a matter of hours.

From the use of composite materials to improve fuel efficiency, to the development of quieter engines and advanced navigation systems, science has played a crucial role in revolutionizing air travel. The aerodynamic design of modern airplanes allows them to achieve incredible speeds while maintaining stability and safety.

This not only makes air travel more convenient for passengers but also reduces the environmental impact of aviation.

Automotive Engineering

The field of automotive engineering has witnessed tremendous advancements, making our cars safer, more efficient, and more comfortable. Science has enabled the development of innovative safety features such as airbags, ABS brakes, and collision avoidance systems, which have significantly reduced the number of accidents and saved countless lives.

The use of lightweight materials and aerodynamic designs has made cars more fuel-efficient, reducing greenhouse gas emissions. Additionally, the integration of GPS technology and smart infotainment systems has made navigation and entertainment more convenient for drivers and passengers alike.

Traffic Optimization Systems

With the increasing number of vehicles on the road, traffic congestion has become a major issue in many cities around the world. Science has played a vital role in developing traffic optimization systems that help manage and reduce congestion.

These systems use advanced algorithms and real-time data to analyze traffic patterns and suggest the most efficient routes for drivers. By optimizing traffic flow, these systems not only save time for commuters but also reduce fuel consumption and air pollution.

Examples of such systems include smart traffic lights, intelligent transportation systems, and traffic management apps.

Supply Chain Logistics

Supply chain logistics involves the management and coordination of the flow of goods and services from the point of origin to the point of consumption. Science has revolutionized this field by introducing innovative technologies and processes that improve efficiency and reduce costs.

For example, the use of barcode scanning, RFID tags, and GPS tracking has made inventory management more accurate and streamlined. Advanced analytics and predictive modeling help optimize routing and scheduling, ensuring timely delivery while minimizing transportation costs.

These innovations have transformed the way goods are transported, making supply chains more efficient and responsive to customer demands.

Insights into Our World

Science plays a fundamental role in our daily lives, often in ways we may not even realize. From the stars in the sky to the products we use, science provides us with valuable insights and understanding. Let’s explore some examples of how science impacts our everyday activities.

Astronomy and Space Science

Have you ever looked up at the night sky and marveled at the stars? Astronomy, the study of celestial objects and phenomena, helps us understand the vastness of the universe. Through telescopes and satellites, scientists have made groundbreaking discoveries about galaxies, planets, and even the origins of the universe itself.

Websites like NASA offer a wealth of information and breathtaking images that bring the wonders of space closer to us.

Physics Principles at Work

Physics is the study of matter and energy, and its principles can be found in many aspects of our daily lives. For example, the laws of motion explain why objects fall to the ground, why vehicles move, and why we can ride a bicycle.

Understanding these principles allows us to design safer cars, build sturdy bridges, and even enjoy thrilling roller coaster rides. Physics is not just for scientists in labs; it’s all around us!

Earth Sciences – Climate, Seismology

Earth sciences, such as climatology and seismology, provide us with valuable knowledge about our planet. Climate science helps us understand the changes happening in our environment and the impact of human activities on the Earth’s climate.

Seismology, the study of earthquakes, allows us to monitor and predict seismic activity, helping to save lives and minimize damage. Websites like climate.gov and USGS offer comprehensive information on these topics.

Chemistry in Everyday Products

Chemistry is present in countless products we use every day, from cleaning supplies to personal care items. For instance, the chemical reactions that occur in batteries power our smartphones and other electronic devices.

Additionally, the development of new materials and pharmaceuticals relies heavily on chemical research. Understanding the principles of chemistry allows us to create safer and more efficient products. Websites like American Chemical Society provide valuable resources on the role of chemistry in our daily lives.

Science is an integral part of our lives, providing us with knowledge and improving our understanding of the world around us. Whether it’s exploring the mysteries of space, harnessing the power of physics, studying our planet’s climate, or utilizing chemistry in everyday products, science impacts our daily activities in profound ways.

As this extensive list of examples shows, science fundamentally shapes our daily lives in modern society. Cutting-edge innovations that enhance how we live, work, communicate, travel, stay healthy, and understand the world all stem from scientific discovery.

Fields like physics, chemistry, biology, astronomy, and engineering create astounding technologies, life-saving medications, and solutions for sustainability. They also unlock deeper insights into our own bodies, the environment, and the universe around us.

So whether you’re video chatting on your phone, cooking dinner, driving your car, or just breathing – you have science to thank! Our modern world simply would not function without the dedicated work of scientists pushing boundaries every day.

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Research and Its Importance for Daily Life Essay

Introduction, impact of research, qualities of effective research, role of beliefs and values.

Research plays an important role in science. This is normally done to obtain detailed knowledge about certain aspects before an invention. Scientific research involves the study of diseases and other parameters to invent medicine and vaccines. Therefore, without research, there will be no inventions and therefore a big blow to health. Essentially research fulfils purposes that are designed before the exercise. However, apart from that, research has other implications on reality and daily lives. As a result, the effects of research go beyond the purpose it is meant for. This paper aims to take an analytical look at the concept of research. The paper will begin with a detailed look at the concept of research. Thereafter, the several similarities between different aspects of research will be analyzed. The impact of research on our daily life will also be reviewed.

Research has a lot of impact on the daily functioning of life. First and foremost, research leads to a better life by producing results that can be used to make life better. Especially as far as scientific research is concerned, the invention of vaccines and medicines makes diseases to be less of a threat to society (Calderon & Slavin 2001). Therefore, through the process of research, various methods of handling life’s problems and making the world a better place to live in are facilitated. Secondly, the very process of research affects society in several ways. The impact of the process of research has two dimensions.

The first part is the negative part in which the process of research has certain consequences for society. Unethical practices harm society. Since research is done on people in society, the practices adopted by the researchers have a lot of impacts. Scientific research has left some people with serious illnesses and injuries sometimes; it is like experimenting with people’s life. However, the process of research also has positive effects on society (McGill 1981). This is mainly because of employment opportunities, awareness and education. Research offers vast opportunities to the members of society to learn and obtain understanding about certain issues. At the same time, the participants of the research are remunerated making them earn a living from the same.

Several factors denote effective and valid research. To conduct valid or effective research, therefore, several considerations must be in place. First is the aspect of ethics, for research to be valid it must be conducted ethically. This involves the practices adopted for the research (Cresswell 2003). If the research involves risks, this must be communicated to the participants in advance. At the same time plans must be in place to compensate all those that will be affected in the course of the research. The disbursing of information is necessary before the research. This is important to take care of deception which is rampant in research. In general, proper preparation and education of the participants is the key to successful research. Another crucial requirement is the availability of resources for research.

Several forms of research involve a different processes. As a result, not all forms of research involve vigour. For instance, scientific research on diseases is more demanding than research on recreational issues. This is due to the context of the studies and the parameters involved. For instance, scientific research involves several processes and procedures which tend to take more resources. Recreational issues, on the other hand, are less involved due to the nature of the subject. The research can therefore be conducted with much ease.

Beliefs and values have a lot of impact on the process of research. People’s beliefs, therefore, influence the outcome and process of research. This is due to the relevance that beliefs and values have on people’s perception and philosophy of life. For instance, certain topics are considered sacred and secret in certain societies (Bryant 2005). Their beliefs don’t allow them to discuss certain things. Therefore in the process of collecting information from such people, it becomes very difficult to deal with them. People’s values also play a huge role. Some people are flexible in certain areas than others. Therefore, when conducting research one must understand the values of all participants. This is because their values determine how they approach certain issues. Religion plays a great role in determining the beliefs and values of people.

Research is part and parcel of life, in fact without research life will not be as it is. To live better life research is necessary; this is because research leads to innovation and invention. As far as science is concerned research leads to the invention of vaccines and drugs. Other areas of research also lead to a better understanding of the concepts involved. However, it is not only the results of research that benefit society but also the process of research. Some several opportunities and benefits that come with the process of research. As a result, the role of research in society goes beyond its real purpose. For research to be effective and valid several factors must be considered. Chief among them is the aspect of ethics. Different forms of research involve different forms of approaches. As a result, certain forms of research are more demanding than others. The influence of values and beliefs is notable as far as research is concerned. The paper has discussed the concept of research in detail. The process and impact of research have also been discussed.

Bryant, M. (2005). Managing an Effective and Ethical Research Project . London: Berrett-Koehler Publishers.

Calderon, M. & Slavin, R. (2001). Effective programs for Latino students. New York: Routledge.

Cresswell, J. (2003). Research design: qualitative, quantitative, and mixed-method approaches. New York: SAGE.

McGill, N. (1981). Effective research: a handbook for health planners. Washington: Institute for Health Planning.

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Using the Scientific Method in Everyday Life

Curiosity is a very important aspect of my life. One of the ways that I foster curiosity is by keeping an open mind and solving new problems. Whenever I am facing a new problem, I try to use the scientific method. Using the scientific method in your daily life can help your problem solving process. 

Let’s go into what the scientific method is, and then discuss how we can implement it in our everyday life.

Scientific Method:

Observation (or find out what the problem is).

The first step in the scientific method is observation. Observing the world around you I’ll keep you in the moment and Identify what the problem is. The ways you can do this is by answering the question who, what, when, where, how, and why. Whether this is part of an actual scientific experiment or just a problem in your daily life, really step back and assess what the actual issue is.

Research the Topic (or gain background information)

Once you have identified the problem, look into what it could be that is causing the problem. Research other possible causes that are out there, and include various different sources in your study. Not only should you consider the content, consider the author and their background and what their intentions for the study might be. This will help you ensure you also gather information from reliable sources.

Form a Hypothesis (or figure out what you think will happen)

Next step in the scientific method is forming a hypothesis. A hypothesis is an educated prediction based on the information you have and what you think will happen. A hypothesis usually comes in the form of if-then statements. For example if “x” happens, then “y” will happen.

Test with an Experiment (or find ways to solve the issue)

After you have gathered information, create a small experiment to test that hypothesis. Also make sure that you have a control group (the untested group) to measure more accurate results. Having this untested group will give you a baseline.

Analyze the Data (or take time to review)

Once you have run through a full cycle of your experiment, then review and analyze the results from that experiment. You can be honest about what things went well, and what you would do differently next time. When analyzing it, use your hypothesis as a guide for measuring your results.

Finalize Conclusion and Report Findings (or communicate and share with others)

This step is where you will finalize your conclusion. Whether you report this for a school project or an employer, reporting your findings can help others learn from your mistakes and trials! Sharing your experience with others can be a very valuable asset. After you report the findings, check out what the community may be saying and see other valuable lessons you can learn.

If this is something you are truly invested in, try your experiment again! You can change your hypothesis, change the way you conduct the experiment, etc. 

If you are going through any problem in life (even if it may not be scientific), it is important to step back and assess. This process can be a great tool to address whatever problem you may be facing. Remember to keep an unbiased view and always do your best to solve the issue. 

Here is a downloadable worksheet for all those who want to use the scientific method. If you are currently wanting to try an experiment, check out my blog post on citizen science projects than anyone can do.

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Home — Essay Samples — Education — Research — Usage Of The Scientific Method In Our Everyday Life

Usage of The Scientific Method in Our Everyday Life

• Categories: Research Theory

About this sample

Words: 591 |

Published: Mar 19, 2020

Words: 591 | Page: 1 | 3 min read

• Observation
• Share result.

Works Cited

• American Psychological Association. (2020). Publication Manual of the American Psychological Association (7th ed.). American Psychological Association.
• Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage Publications.
• Easterby-Smith, M., Thorpe, R., & Jackson, P. R. (2015). Management Research (5th ed.). Sage Publications.
• Gall, M. D., Gall, J. P., & Borg, W. R. (2021). Educational Research: An Introduction (11th ed.). Pearson.
• Leedy, P. D., & Ormrod, J. E. (2021). Practical Research: Planning and Design (12th ed.). Pearson.
• Locke, L. F., Spirduso, W. W., & Silverman, S. J. (2021). Proposals That Work: A Guide for Planning Dissertations and Grant Proposals (8th ed.). Sage Publications.
• McMillan, J. H., & Schumacher, S. (2019). Research in Education: Evidence-Based Inquiry (8th ed.). Pearson.
• Neuman, W. L. (2013). Social Research Methods: Qualitative and Quantitative Approaches (7th ed.). Pearson.
• Trochim, W. M. K., & Donnelly, J. P. (2008). The Research Methods Knowledge Base (3rd ed.). Atomic Dog Publishing.
• Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage Publications.

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