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Video: Understanding the Scientific Method

Published on Jul 09, 2020 · Last Updated 2 years 8 months ago
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Presented by: Christopher Rock, PhD

Learn how to create a hypothesis, as well as the steps of hypothesis-driven research and why these steps are important.

Resources: Understanding the Scientific Method Worksheet

Transcript

Slide 1: Title

  • Hello and welcome to the CHOP-RISES skill blitz on understanding the scientific method.
  • I am Christopher Rock and will be guiding you through the topic.

Slide 2: Learning Objectives

  • There are four goals for this skill blitz.
  • First, we will introduce you to different types of scientific research.
  • Second, we will detail the steps of hypothesis-driven research, with emphasis on why their structure is important to good science.
  • Next, we will explore what makes a strong hypothesis.
  • Lastly, we will cover two advanced research topics, experimental controls and sampling bias.

Slide 3: Quote

  • To kick off this blitz, I would like to share a line said by Sir Arthur Conan Doyle's character, Sherlock Holmes: “I have no data yet. It is a capital mistake to theorise before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”

Slide 4: Facts and Theory

  • Scientific research can be described as acquiring facts and generating theorems to connect them.
  • When theory leads, we are forced to nudge or ignore facts altogether when they do not follow the theory.
  • However, when the facts lead, the theory becomes stronger and more accurate. With this mindset, theories can evolve as more facts come forth.

Slide 5: Types of Scientific Research

  • We will explore three types of scientific research in this blitz: Hypothesis-driven, Discovery-driven, and Descriptive.
  • Hypothesis-driven research evaluates a proposed process or explanation (a hypothesis), finding the likelihood of the proposal being true.
  • An example of hypothesis-driven research is an animal study questioning if a new blood pressure drug is more effective than those currently available.
  • Discovery-driven research similarly has an objective or process to explain but the study runs with no particular explanation in mind. Rather, the explanation comes forth from the analysis, i.e. is discovered.
  • An example of discovery-driven research is analyzing a large set of patient data and finding traits, like diabetes or age, that increase risk of heart failure.
  • Descriptive research operates with no goal beyond documenting the observations from the study.
  • An example of descriptive research would be a case study sent to a clinical journal describing how a pacemaker caused an unusual response in a patient.
  • Going forward in this blitz, we will focus on hypothesis-driven research.

Slide 6: Steps of Hypothesis-Driven Research

  • Hypothesis-driven research starts with an observation or question that prompts the interest for the study.
  • For example, does having a regular sleep cycle improve learning?
  • Before going any further, we will conduct background research to frame our question and find out what is currently known and currently not known about the link between sleep and learning.
  • With this knowledge, we will formulate a hypothesis, this is the directed inquiry that drives the experiment. It is a specific statement that will be either proven or disproven by the end of the experiment. In this case, we hypothesize that disrupting the day-night cycle will cause an increase in learning time.
  • Having established our hypotheses, we will then design and run an experiment to test that hypothesis. An example experiment would have rats learn a maze pattern and see how their solving speed is impacted by the day-night cycles they are exposed to.
  • Once the experiment is conducted, the raw data must be analyzed. Typically for hypothesis-driven research, you would use statistical tests to determine how likely the hypothesis is to occur. In our example, this would be the odds of changing the day-night cycle having an effect on the learning time based off of our results.
  • Once the data is analyzed, we have to reach a decision to either accept or reject our hypothesis. If the odds having no effect are sufficiently small, typically less than 5%, we would then go ahead and accept our hypothesis.
  • It is important to note that this is a circular process. Once we establish our conclusion, new questions will crop up like “is this effect stronger in children or adults?”.
  • Lastly, the process is not complete if you do not share the findings with the scientific community. Your results may be the observations that start off someone else’s cycle!

Slide 7: Importance of Structure to Research

  • So why is it important that studies are conducted in such a structured manner?
  • First, the structure improves the effectiveness of the study. By researching, you make sure your work builds off previous studies but is also not redundant. By keeping organized, your results directly address the question that prompted the study.
  • Second, the structure strengthens the rigor and reproducibility of the study. The confidence in our conclusions increases by being meticulous in our approach. An experiment has no value if the results cannot be replicated by other parties.
  • Last but not least, by following a standard approach, other researchers can better utilize your findings, making your work much more impactful. You also benefit as this means other researchers will be more interested in working with you in the future.

Slide 8: Creating a hypothesis

  • A hypothesis is the fulcrum by which your study pivots around; so effective studies need strong hypotheses.
  • Unlike an experimental question, which is somewhat vague and open ended, a hypothesis is a statement by which the results will be evaluated.
  • A hypothesis should be specific to the variables of interest of the study.
  • A hypothesis needs to be detailed so the exact effect that is being evaluated is known.
  • A hypothesis must be a statement that can be tested and proven false, otherwise, no conclusion to accept or reject it can be made.

Slide 9: Weak and strong hypothesis

  • Now we will explore two weak hypothesis and see how we strengthen them.
  • First hypothesis: smoking causes lung cancer.
  • This hypothesis is weak on the specifics, does not have enough details for an experiment, nor is it easily tested.
  • A stronger hypothesis would be: Lung cancer incidence is greater in smokers than the general population.
  • This statement has a specific variable we are evaluating, details what we are looking at, and can easily be proven either true or false.
  • Our second example hypothesis: Cell phones do not cause Alzheimer’s disease.
  • This hypothesis is very vague, has only limited details, and it would be hard to prove false, making it a weak hypothesis.
  • A more effective hypothesis would be: Amount of cell phone usage does not correlate with the age of onset of Alzheimer’s disease.
  • This hypothesis clearly specifies what is being evaluated, details what analysis is being done, and can readily be tested, making it a stronger hypothesis.

Slide 10: Experimental controls

  • We will move on to our first advanced topic, experimental controls.
  • Establishing controls helps determine what is the normal response or effect.
  • Especially in biology, there are many responses to treatment or environment, such as changing temperature. Therefore, we want to minimize the effects of variables that are not part of the study.
  • You have likely heard of the placebo effect, how just the act of taking medicine induces a response. This is why drug studies will have a placebo group that takes medicine designed to have no effect.
  • If you are testing some treatment, the control group may be what is the normal care given to compare against.

Slide 11: Control Example

  • Let’s say we are conducting a clinical trial for an anti-insomnia medication.
  • If we get in the results showing improvement from the test group, how can we be sure any changes are a result of the medication and not some other effect?
  • Answer: Add in a control group.
  • This group would receive a placebo, a treatment without the active drug.
  • Now, think on this: With this control group, what does it mean for our drug if the control group does not show any improvement in sleep? What does it mean if the control group does report improved sleep?

Slide 12: Sampling Bias

  • The second advanced topic this blitz will cover is sampling bias.
  • Sampling bias occurs when the study group formed has a distribution that does not properly represent the population the study is about.
  • For example, you are studying a hard-shelled candy that comes in 5 colours, evenly distributed. If your sample group of 30 looks like this, you likely have some bias in your sampling method with some colours being overrepresented and other colours being underrepresented. This bias would undermine any conclusions you make about the candy in general.
  • Sampling bias can come from many sources.
    • One source is the criteria for how the subjects of the study are selected or excluded. For example, a study that looks at tissues from dead people will be overrepresented by sick persons.
    • Next, if the study requires those involved to comply with requests, people who do not do so become excluded.
    • Lastly, there are instances where there is intentional sampling bias in order to manipulate results to get a specific conclusion. This misconduct not only introduces false data but also undermines people’s confidence in the processes.
  • At the end of the day, all studies will have limitations, for example: almost every study group will be smaller than the general population, so some individuals will not be represented. Not all selection bias can be eliminated. Therefore, as researchers we strive to minimize bias if possible, to acknowledge bias when we cannot, and to document our process in detail so biases we are unaware of can be found.

Slide 13: Learning objectives

  • So let’s check through our learning objectives.
  • We have gone over 3 different types of scientific research: hypothesis-driven, discovery-driven, and descriptive.
  • We walked through the steps of hypothesis driven research and discussed the importance of their structure.
  • We have defined what makes a strong hypothesis.
  • And we have been introduced to the concepts of experimental controls and sampling bias alongside the role they play in scientific research.

Slide 14: References and Extra Resources

  • I hope this blitz has proved useful.
  • Here are reference links to the slide figures as well as some supplemental reading on the subjects.
  • I would like to thank Dr. Sara Rego for organizing this blitz and providing consultation on the content.
  • I wish you the best of luck with your own studies wherever they may take you!