Science and Biology

What is biology? In simple terms, biology is the study of living organisms and their interactions with one another and their environments. This is a very broad definition because the scope of biology is vast. Biologists may study anything from the microscopic or submicroscopic view of a cell to ecosystems and the whole living planet (Figure 1). Listening to the daily news, you will quickly realize how many aspects of biology we discuss daily. For example, recent news topics include Escherichia coli (Figure 2) outbreaks in spinach and Salmonella contamination in peanut butter. Other subjects include efforts toward finding a cure for AIDS, Alzheimer’s disease, and cancer. On a global scale, many researchers are committed to finding ways to protect the planet, solve environmental issues, and reduce the effects of climate change. All of these diverse endeavors are related to different facets of the discipline of biology.

But what then is science, exactly? What does the study of biology share with other scientific disciplines? We can define science (from the Latin scientia, meaning “knowledge”) as the knowledge that covers general truths or the operation of general laws, especially when acquired and tested by the scientific method. Science is both a body of knowledge about the natural world and a reliable process for building that knowledge. What makes science different from other ways of understanding the world is how scientists gather and evaluate evidence.

Think about a question that might arise in your daily life: “Why do some people get malaria while others living in the same area don’t?” You might guess it’s due to genetics, diet, or pure luck. But how could you find out which explanation is actually correct? This is where the scientific method becomes essential—it provides a systematic way to test our ideas against evidence from the real world.

Science relies on testing ideas with evidence gathered from the natural world; a hallmark of science is exposing ideas to testing. Scientists don’t just accept explanations because they sound reasonable or because an authority figure promotes them. Instead, they design careful studies to determine whether their ideas match what actually happens in nature.

Natural Sciences

What would you expect to see in a museum of natural sciences? Frogs? Plants? Dinosaur skeletons? Exhibits about how the brain functions? A planetarium? Gems and minerals? Maybe all of the above? Science includes such diverse fields as astronomy, biology, computer sciences, geology, logic, physics, chemistry, and mathematics (Figure 3). However, scientists consider those fields of science related to the physical world and its phenomena and processes in natural sciences. Thus, a museum of natural sciences might contain any of the items listed above.

There is no complete agreement when it comes to defining what the natural sciences include, however. For some experts, the natural sciences are astronomy, biology, chemistry, earth science, and physics. Other scholars choose to divide natural sciences into life sciences, which study living things and include biology, and physical sciences, which study nonliving matter and include astronomy, geology, physics, and chemistry. Some disciplines, such as biophysics and biochemistry, build on both life and physical sciences and are interdisciplinary. Some refer to natural sciences as “hard science” because they rely on the use of quantitative data. Social sciences that study society and human behavior are more likely to use qualitative assessments to drive investigations and findings.

Not surprisingly, the natural science of biology has many branches or subdisciplines. Cell biologists study cell structure and function, while biologists who study anatomy investigate the structure of an entire organism. Those biologists studying physiology, however, focus on the internal functioning of an organism. Some areas of biology focus on only particular types of living things. For example, botanists explore plants, while zoologists specialize in animals.

Basic Science and Applied Science

The scientific community has been debating for the last few decades about the value of different types of science. Is it valuable to pursue science for the sake of simply gaining knowledge, or does scientific knowledge only have worth if we can apply it to solving a specific problem or to better our lives? This question focuses on the differences between two types of science: basic science and applied science.

Basic science or “pure” science seeks to expand knowledge regardless of the short-term application of that knowledge. It is not focused on developing a product or a service of immediate public or commercial value. The immediate goal of basic science is knowledge for knowledge’s sake, although this does not mean that, in the end, it may not result in a practical application.

In contrast, applied science, or “technology,” aims to use science to solve real-world problems, making it possible, for example, to improve a crop yield, find a cure for a particular disease, or save animals threatened by a natural disaster. In applied science, the problem is usually defined for the researcher.

Many scientists think that a basic understanding of science is necessary before researchers develop an application; therefore, applied science relies on the results that researchers generate through basic science. The bed net intervention only became possible because of prior basic research that revealed how malaria is transmitted, how insecticides affect mosquitoes, and how mosquito behavior influences disease transmission.

While scientists usually carefully plan research efforts in both basic science and applied science, note that some discoveries are made by serendipity, that is, by means of a fortunate accident or a lucky surprise. Alexander Fleming discovered penicillin when he accidentally left a petri dish of Staphylococcus bacteria open. An unwanted mold grew on the dish, killing the bacteria (Figure 4). Fleming’s curiosity to investigate the reason behind the bacterial death, followed by his experiments, led to the discovery of the antibiotic penicillin. Even in the highly organized world of science, luck—when combined with an observant, curious mind—can lead to unexpected breakthroughs.

It is true that there are problems that demand immediate attention; however, scientists would find few solutions without the help of the wide knowledge foundation that basic science generates.

The Process of Science

The Scientific Method

Scientists study the living world by posing questions about it and seeking science-based responses. Known as the scientific method, this approach is common to other sciences as well. The scientific method was used even in ancient times, but England’s Sir Francis Bacon (1561–1626) first documented it (Figure 5). He set up inductive methods for scientific inquiry. The scientific method is not used only by biologists; researchers from almost all fields of study can apply it as a logical, rational problem-solving method.

The scientific process typically starts with an observation (a curious pattern in nature or a problem to solve) that leads to a question. Let’s examine how this process works using a real example that has saved thousands of lives: the use of insecticide-treated bed nets (ITBNs) to prevent malaria.

In the early 1990s, researchers working in sub-Saharan Africa noticed a troubling pattern: malaria was killing hundreds of thousands of children each year, despite existing prevention efforts. Dr. Chris Nevill and his colleagues observed that in areas where people used bed nets, there seemed to be fewer cases of severe malaria. This observation led to an important question: Do insecticide-treated bed nets actually reduce malaria-related death and severe illness in children?

This question arose from careful observation, but answering it required much more than just looking around. The researchers needed to design a rigorous study that could provide reliable evidence. Let’s follow their scientific journey to understand how the scientific method builds trustworthy knowledge.

The scientific method may seem too rigid and structured. It is important to keep in mind that, although scientists often follow this sequence, there is flexibility. Sometimes, an experiment leads to conclusions that favor a change in approach. Often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion. Instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests. Notice, too, that we can apply the scientific method to solving problems that aren’t necessarily scientific in nature.

Summary of Nevill’s Experiment

Title: Insecticide-treated bednets reduce mortality and severe morbidity from malaria among children on the Kenyan coast. 

 

Abstract: New tools to prevent malaria morbidity and mortality are needed to improve child survival in sub-Saharan Africa. Insecticide treated bednets (ITBN) have been shown, in one setting (The Gambia, West Africa), to reduce childhood mortality. To assess the impact of ITBN on child survival under different epidemiological and cultural conditions we conducted a community randomized, controlled trial of permethrin treated bednets (0.5 g/m2) among a rural population on the Kenyan Coast. Between 1993 and 1995 continuous community-based demographic surveillance linked to hospital-based in-patient surveillance identified all mortality and severe malaria morbidity events during a 2-year period among a population of over 11,000 children under 5 years of age. In July 1993, 28 randomly selected communities were issued ITBN, instructed in their use and the nets re-impregnated every 6 months. The remaining 28 communities served as contemporaneous controls for the following 2 years, during which continuous demographic and hospital surveillance was maintained until the end of July 1995. Childhood mortality rates in communities with ITBN were significantly lower than non-ITBN communities (with ITBN = 9.4 per 1000; without ITBN 13.2 per 1000). Similarly, the prevalence of severe, life-threatening malaria among children aged 1-59 months (with ITBN = 11.0 per 1000; without ITBN 20.0 per 1000). These findings confirm the value of ITBN in improving child survival and provide the first evidence of their specific role in reducing severe morbidity from malaria.

Citation: Nevill CG, Some ES, Mung’ala VO, Mutemi W, New L, Marsh K, Lengeler C, Snow RW. Trop Med Int Health. 1996 Apr;1(2):139-46. doi: 10.1111/j.1365-3156.1996.tb00019.x

Proposing a Hypothesis

Recall that a hypothesis is a suggested explanation that one can test. Hypotheses must be falsifiable, meaning they can be proven false by the scientific method. To solve a problem, one can propose several hypotheses.

In the malaria study, the researchers needed to move beyond their initial observation to propose a specific, testable explanation. Based on their understanding of how bed nets work and previous smaller studies, they proposed this hypothesis: “Insecticide-treated bed nets will reduce childhood mortality and severe malaria morbidity compared to communities without treated bed nets.”

This hypothesis has several important characteristics that make it scientific:

1. It’s specific: The hypothesis clearly states what will happen (reduced mortality and morbidity) and in which population (children).
2. It’s testable: Researchers can measure mortality rates and malaria cases in different communities and compare them.
3. It’s falsifiable: If the data showed no difference or higher mortality in communities with bed nets, the hypothesis would be proven wrong.

For every hypothesis, scientists also consider the null hypothesis—the expectation if the proposed explanation is wrong. In this case, the null hypothesis would be: “There will be no difference in childhood mortality and severe malaria morbidity between communities with and without insecticide-treated bed nets.”

Why do scientists always consider the null hypothesis? It helps them think clearly about what their data mean. If they find a difference between groups, they need to determine whether it’s a real effect of their treatment or just random chance. The null hypothesis provides a baseline for comparison.

Once one has selected a hypothesis, the student can make a prediction. A prediction is similar to a hypothesis but it typically has the format “If . . . then . . . .” For the malaria study, the prediction might be: “If insecticide-treated bed nets prevent malaria-carrying mosquitoes from biting children, then communities that receive bed nets will have lower rates of childhood death and severe malaria than communities that don’t receive bed nets.”

Experimental Design

To test a hypothesis, researchers design an experiment or analysis designed to validate or reject the hypothesis. The Nevill study provides an excellent example of how scientists structure experiments to gather reliable evidence.

Variables in Experiments

There are many types of experiments and analyses researchers conduct to test hypotheses. The general structure of most of these experiments or analyses, however, involves examining the effect of one variable on another. A variable is any part of the experiment that can vary or change during the course of the experiment.

In the malaria study:

• Independent variable: The condition the researcher purposefully changes to see how it affects the outcome. Here, it was whether communities received insecticide-treated bed nets or not.
• Dependent variable: The variable of interest that researchers measure to see if the independent variable has an effect. In this study, the dependent variables were childhood mortality rates and severe malaria morbidity rates.

Variables other than the independent variable that might nonetheless affect the dependent variable are referred to as confounding factors. In the malaria study, confounding factors might include differences between communities in access to healthcare, proximity to mosquito breeding sites, socioeconomic status, or other disease prevention measures.

Control and Experimental Groups

The most basic experimental design involves two groups: a control group and an experimental group. The control group represents the unmanipulated study condition, while the experimental group is somehow manipulated to test the effect of the independent variable.

In the Nevill study:

• Experimental group: 28 communities that received insecticide-treated bed nets
• Control group: 28 communities that did not receive treated bed nets (serving as contemporaneous controls)

Otherwise, differences between the groups are limited to reduce any potential confounding variables. The researchers worked hard to ensure that the control and experimental communities were similar in other important ways—they were all rural communities on the Kenyan coast with similar demographics and healthcare access.

Sample Size and Replication

Notice that the researchers didn’t just study one community with bed nets and one without. They studied 28 communities in each group. Why was this important? Sample size refers to the number of observations within each treatment, while replication refers to the number of repeated times the same experiment treatment is tried.

Two factors that play a major role in the power of an experiment to detect meaningful statistical differences are sample size and replication. In general, the bigger the sample size and the more replication, the more confidence a researcher can have in the outcome of their study. With more communities in each group, the researchers could be more confident that any differences they observed were due to the bed nets rather than just random variation between communities.

Randomization

A well-designed experiment will attempt to minimize the effect of confounding factors so that the researcher can be confident that the independent variable is the one causing the change in the dependent variable. The Nevill study used randomization—they randomly selected which 28 communities would receive bed nets and which would serve as controls. This helped ensure that any unknown differences between communities were evenly distributed between the two groups.

VISUAL CONNECTION

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In the example below, the scientific method is used to solve an everyday problem. Order the scientific method steps (numbered items) with the process of solving the everyday problem (lettered items). Based on the results of the experiment, is the hypothesis correct? If it is incorrect, propose some alternative hypotheses.

1. Observation	a. There is something wrong with the electrical outlet.
2. Question	b. If something is wrong with the outlet, my coffee maker also won’t work when plugged into it.
3. Hypothesis (proposed answer)	c. My toaster doesn’t toast my bread.
4. Prediction	d. I plug my coffee maker into the outlet.
5. Experiment	e. My coffee maker works.
6. Result	f. Why doesn’t my toaster work?

^{Answer:
1: C; 2: F; 3: A; 4: B; 5: D; 6: E. The original hypothesis is incorrect, as the coffee maker works when plugged into the outlet. Alternative hypotheses include that the toaster might be broken or that the toaster wasn’t turned on.}

Statistics and Scientific Confidence

To determine if the results of their experiment are significant, researchers use a variety of statistical analyses. The Nevill study produced clear results, but understanding what those results mean—and how confident we can be in them—requires careful analysis.

Statistical analyses help researchers determine whether the observations from their experiments are meaningful or due to random chance. For example, if a researcher observes a difference between the control group and experimental group, should they treat it as a real effect of the independent variable or simply random chance?

The Nevill study found:

• Mortality rates: 9.4 deaths per 1,000 children in communities with bed nets versus 13.2 deaths per 1,000 children in communities without bed nets
• Morbidity rates: 11.0 cases per 1,000 children with bed nets versus 20.0 cases per 1,000 children without bed nets

But are these differences meaningful? A result is considered to have statistical significance when it is very unlikely to have occurred given the null hypothesis. The researchers used statistical tests to determine that these differences were indeed significant, meaning they were very unlikely to be due to random chance alone.

Confidence in any single statistical test depends on the reliability of the underlying data. Many things can affect how confident we are in the results of a given scientific experiment and how confident we are in the final statistical analysis.

For example, several aspects of the Nevill study increased confidence in the results:

• Large sample size: With 28 communities in each group and over 11,000 children monitored, the study had sufficient power to detect meaningful differences.
• Randomization: Random assignment of communities to treatment groups minimized bias.
• Appropriate controls: The control communities were similar to treatment communities in all ways except the bed nets.
• Clear methodology: The researchers clearly described their methods, allowing others to evaluate and potentially replicate the study.

Statistical results themselves are not entirely objective and can depend on many assumptions, including the null hypothesis itself. A researcher must consider potential biases in their analyses, just as they do confounding variables in their experimental design.

Potential limitations of the Nevill study might include:

• Unmeasured confounding variables: Despite randomization, communities might have differed in ways not accounted for (e.g., sanitation practices, proximity to water sources).
• Compliance: Not all families in treatment communities may have used the bed nets consistently.
• Generalizability: Results from rural Kenya might not apply to other settings with different mosquito species or malaria transmission patterns.

Scientific Communication and Peer Review

After conducting an experiment, scientists must share their findings in order for other researchers to expand and build upon their discoveries. The Nevill study exemplifies this principle. The researchers didn’t simply conduct their experiment and keep the results to themselves. Instead, they followed the established process of scientific communication that makes science a community endeavor.

Collaboration with other scientists—when planning, conducting, and analyzing results—are all important for scientific research. There are many ways that researchers communicate the results of their work, ranging from informal conversations with their peers and colleagues to formal presentations at conferences or symposia. The primary way that scientists share results, however, is through publication in scientific journals that are peer-reviewed.

The process of peer review helps to ensure that the research in a scientific work is original, significant, logical, and thorough. In fact, peer review is used to vet research at many different stages of a project, not just the final publication. For instance, most research is funded through grants which require scientists to submit a proposal. These proposal are typically subject to peer review.

Peer-review means that a scientific grant or paper has been vetted by qualified individuals, often experts in the same research area, who judge whether or not the scientist’s work is suitable for funding or publication. For instance, before the Nevill study was published, it was reviewed by other epidemiologists who evaluated:

• Whether the methodology was sound
• Whether the statistical analyses were appropriate
• Whether the conclusions were supported by the data
• Whether the work was original and significant

The process of peer review helps to ensure that the research in a scientific paper or grant proposal is original, significant, logical, and thorough. However, peer review is not perfect—it can be influenced by bias, and even peer-reviewed studies sometimes contain errors or reach incorrect conclusions. Think of peer review as one step in sharing research with the broader community. It is a critical step, but not the last step in building scientific knowledge.

Publishing and Sharing Scientific Results

A scientific paper is very different from other forms of writing. There are fixed guidelines when it comes to presenting scientific results. First, scientific writing must be brief, concise, and accurate. A scientific paper needs to be succinct but detailed enough to allow peers to reproduce the experiments.

A scientific paper typically consists of four main sections—introduction, materials and methods, results, and discussion. This structure is sometimes called the “IMRaD” format. There are usually acknowledgment and reference sections as well as an abstract (a concise summary) at the beginning of the paper. There might be additional sections depending on the type of paper and the journal where it will be published. For example, some review papers require an outline.

Understanding this structure helps you evaluate scientific claims more critically:

Introduction: The introduction starts with brief, but broad, background information about what is known in the field. A good introduction also gives the rationale of the work. It justifies the work carried out and also briefly mentions the end of the paper, where the researcher will present the hypothesis or research question driving the research.

Materials and Methods: The materials and methods section includes a complete and accurate description of the substances the researchers use, and the method and techniques they use to gather data. The description should be thorough enough to allow another researcher to repeat the experiment and obtain similar results, but it does not have to be verbose. This section will also include information on how the researchers made measurements and the types of calculations and statistical analyses they used to examine raw data. Although the materials and methods section gives an accurate description of the experiments, it does not discuss the results.

Results: The results section narrates the findings without any further interpretation. It includes a written summary of the results along with key tables or graphs.

Discussion: The discussion section is where results are interpreted and explained In the context of the original question, as well as the broader literature. It is in the discussion section where researchers attempt to make sense of what they found, It’s possible implications for the field at large, and strengths and limitations of the research.

In addition to the Discussion section, some papers include a separate Conclusion section. The conclusion section is typically short and summarizes the key takeaway from the study. It is also a place to raise questions, speculation, and suggestions for future research.

Building Scientific Consensus and Knowledge

The Nevill study and similar research provided the scientific foundation for one of the most successful public health interventions in recent history, the widespread dissemination and use of bednets to prevent malaria. However, understanding how a single study becomes global health policy requires us to examine how scientific consensus builds over time through multiple layers of evidence.

Individual studies, no matter how well-designed, rarely lead directly to major policy changes. Instead, the scientific community builds confidence through what researchers call a hierarchy of scientific evidence. Think of this hierarchy as a pyramid, with individual case reports and expert opinions at the base, and systematic reviews and meta-analyses at the top. The higher up the pyramid, the stronger our confidence in the scientific findings (Figure 6) [1].

The Nevill study represented a significant advance because it was a randomized controlled trial—a type of study that sits high on this evidence pyramid. However, even stronger evidence comes from meta-analyses (systematic reviews), which combine data from multiple similar studies to provide more robust conclusions than any single study could offer alone.

A meta-analysis is a statistical technique that combines results from multiple independent studies addressing the same research question. Rather than simply reading several studies and summarizing their conclusions, meta-analysis uses mathematical methods to pool the actual data from different studies, creating a larger, more powerful analysis than any individual study could provide.

In the case of malaria bed nets, Dr. Christian Lengeler conducted a meta-analysis in 2004 that combined results from five randomized controlled trials, including the Nevill study [2]. The combined analysis showed that insecticide-treated bed nets reduced child mortality by approximately 18% overall, a finding that was both statistically significant and remarkably consistent across different studies and locations.

Why is meta-analysis so powerful? By combining multiple independent studies, researchers can overcome the limitations and potential biases of any single investigation.

Building Scientific Consensus Through Reproducibility

The progression from individual studies to consensus illustrates how science builds reliable knowledge through cumulative evidence. After the Nevill study demonstrated promising results, other research groups conducted similar trials in different African countries including Ghana, Burkina Faso, and Kenya. Each study used slightly different methods and populations, but all reached similar conclusions about the effectiveness of insecticide-treated bed nets.

This pattern of reproducibility across different settings and research groups is crucial for building scientific confidence. When multiple independent studies reach the same conclusion using different populations and slightly different methods, it suggests that the findings represent real biological effects rather than coincidental results or methodological artifacts.

Of course, with the Nevill study, it would be very difficult to reproduce exactly what they did in the exact same communities, as these communities have changed over the decades since the study was conducted. This is often a challenge in biological research, where the systems people are studying are dynamic and changing, including human populations. Despite this challenge, this reproducibility principle is crucial for building scientific confidence; when multiple independent research groups can reproduce similar results using the same methods in comparable settings, it provides strong evidence that the findings are reliable and not due to chance or hidden biases.

What Is a Scientific Theory?

With enough evidence, a concept or explanation can become the highest form of scientific understanding: a theory [3].

People commonly use the word theory to describe a guess or hunch about why something happens. For example, you might say, “I think a woodchuck dug this hole in the ground, but it’s just a theory.” Using the word theory in this way is different from the way it is used in science. A scientific theory is not just a guess or hunch that may or may not be true. In science, a theory is an explanation that has a high likelihood of being correct because it is so well supported by evidence.

A scientific theory is a broad explanation of events that is widely accepted by the scientific community. To become a theory, an explanation must be strongly supported by a great deal of evidence. Examples of scientific theories include the theory of evolution, germ theory of disease, and atomic theory of matter.

What is a scientific theory?

From Evidence to Global Policy

Based on this meta-analytic evidence, the World Health Organization issued formal recommendations for insecticide-treated bed nets as a core malaria prevention strategy. The WHO’s recommendation triggered a massive global response, with international donors providing billions of dollars to purchase and distribute bed nets across sub-Saharan Africa. Organizations like the Global Fund to Fight AIDS, Tuberculosis and Malaria, the President’s Malaria Initiative, and numerous non-governmental organizations launched coordinated campaigns to achieve universal bed net coverage.

This transformation from laboratory research to global health policy illustrates one of science’s most important functions—providing evidence-based guidance for decisions that affect millions of lives. The scientific method’s emphasis on rigorous testing, peer review, and replication helped ensure that the massive resources devoted to bed net programs were based on solid evidence rather than wishful thinking or untested assumptions.

When Scientific Evidence Meets Real-World Complexity

However, the transition from controlled scientific studies to real-world implementation also reveals important limitations in how scientific knowledge translates to practice. Scientific studies are designed to test whether an intervention can work under carefully controlled conditions, but they cannot guarantee that the intervention will work equally well when implemented in complex, real-world settings.

A sobering example comes from subsequent research examining bed net programs after their widespread implementation. A comprehensive systematic review found that insecticide-treated bed nets were only effective in approximately 60% of countries where mass distribution programs were implemented. This finding doesn’t invalidate the original scientific evidence—the controlled trials clearly demonstrated that bed nets can save lives when used properly. Instead, it highlights the complex factors that influence whether scientific interventions succeed when scaled up to entire populations.

Several factors contributed to this implementation gap. In some locations, people received bed nets but didn’t use them consistently, perhaps because they found them uncomfortable or because they didn’t fully understand malaria transmission. In other areas, bed nets were diverted from their intended use—some families used them for fishing nets or garden protection rather than sleeping under them. Additionally, over time, mosquitoes in some regions developed resistance to the insecticides used in the nets, reducing their effectiveness.

This experience illustrates an important principle: scientific evidence tells us what’s possible under ideal conditions, but successful real-world implementation requires understanding and addressing social, economic, and cultural factors that influence human behavior. The most rigorous scientific study cannot account for all the complexities that emerge when interventions are implemented across diverse communities with different resources, beliefs, and practices.

The Self-Correcting Nature of Science

The bed net story also demonstrates how science is self-correcting over time. When implementation studies revealed that bed nets weren’t working equally well everywhere, researchers didn’t abandon the intervention. Instead, they conducted new studies to understand why effectiveness varied across settings. This research led to improved distribution strategies, better community education programs, and the development of new types of bed nets with longer-lasting insecticides.

This process of continuous refinement based on new evidence exemplifies how science builds increasingly reliable knowledge over time. Individual studies provide important pieces of the puzzle, meta-analyses help identify consistent patterns across multiple studies, and implementation research reveals how scientific findings translate to real-world applications. Each type of research contributes to a more complete understanding that guides both scientific knowledge and practical policy decisions.

This demonstrates how rigorous scientific methodology can lead to practical applications that benefit human health, while also illustrating the ongoing process of refinement that characterizes scientific progress.

Evaluating Scientific Claims in Media

Scientific knowledge helps us make decisions that affect our lives every day. However, when scientific findings are reported in news media or on social media, important details about methodology and limitations are often simplified or omitted. Understanding the scientific method helps you evaluate such claims more critically.

When you encounter a science-related claim in the media, consider these questions:

1. What was the actual study? Look for the original research paper, not just news reports about it.
2. What was the sample size? Small studies are more likely to be influenced by random chance.
3. Was there a control group? Studies without proper controls cannot establish cause-and-effect relationships.
4. Who conducted the research? Consider potential conflicts of interest or bias.
5. Has the study been peer-reviewed? Preliminary results presented at conferences or in press releases may not have undergone rigorous review.
6. Have the results been replicated? Single studies, even good ones, can be misleading. Look for independent confirmation.

Understanding the process of science empowers you to be a more informed consumer of scientific information and to make better decisions based on evidence rather than speculation.

Conclusion

Science is ongoing; answering one scientific question frequently leads to additional questions to be investigated. The Nevill study answered an important question about bed nets and malaria, but it also raised new questions: How long do the insecticides remain effective? What happens if mosquitoes develop resistance? Can similar approaches work for other vector-borne diseases?

Scientific knowledge is open to question and revision as new ideas surface and new evidence is discovered. As new evidence emerges about insecticide resistance or mosquito behavior, recommendations for bed net use may be refined. This process of continuous questioning and revision is not a weakness of science—it’s one of its greatest strengths.

The scientific method provides a reliable way to understand the natural world, but it requires patience, careful methodology, and honest evaluation of evidence. By understanding how science works, you become better equipped to evaluate scientific claims, make informed decisions, and perhaps even contribute to scientific knowledge yourself.

Review of the scientific process

References and Sources:

• Bartee, L., Shriner, W., and Creech C. (n.d.) Principles of Biology. Pressbooks. Retrieved from https://openoregon.pressbooks.pub/mhccmajorsbio/chapter/using-credible-sources/
• Clark, M.A., Douglas, M., and Choi, J. 2018. Biology 2e. OpenStax. Retrieved from https://openstax.org/books/biology-2e/pages/1-introduction
• Nevill CG, Some ES, Mung’ala VO, Mutemi W, New L, Marsh K, Lengeler C, Snow RW. Trop Med Int Health. 1996 Apr;1(2):139-46.
• Lengeler C. Insecticide-treated bed nets and curtains for preventing malaria. Cochrane Database Syst Rev. 2004;(2):CD000363.