health study lab report

Generally speaking, for any good study, the subjects should be as uniform as possible (age, gender, size….) to try to eliminate the potential for other influential variables. These subjects then need to be randomly assigned to the experimental and control groups; there can be no bias in assigning certain individuals to the experimental group.

When observations confirming a hypothesis accumulate through repeated experimentation, conducted by different researchers, the hypothesis becomes a theory. When a theory is consistently supported by experiments and remains unchallenged over many years, it is called a law. It is important to remember that the scientific method rarely, if ever, establishes irrefutable truths. The factual basis of science is limited to the circumstances under which the experiments were conducted.

Also, science cannot prove that something does not exist or that there is never a certain effect. For example, the scientific method cannot prove that there is never an adverse effect of a particular drug; it can only be used to assess the probability that there is no adverse effect of a drug.

These are the basic steps of the scientific method:

Make an Observation

Form a Hypothesis

Design experiment and test hypothesis

Interpret results (determine if data are biased, accept or reject hypothesis)

Refine experiment and/or repeat experiment, modify hypothesis if necessary

Accept repeatable results as theory

Lab Report – The Scientific Method

Before proceeding, carefully read through the lab exercise. It would be best if you study from it and take the quiz as well. You should also read the section on the scientific method in the first chapter in your lecture textbook so that you have a solid background in the subject matter.

Based on observations that you have made in the past, or relationships that you might have heard about, you will develop and test your own hypothesis. Your challenge is to hypothesize a relationship between certain populations of people and some quantifiable (numerical) anatomical or physiological feature.

Make sure that your hypothesis is testable and that you will be able to collect relevant data. Remember to control as many other variables as possible. All of your subjects should be in good health, the same gender and as close to the same age and height as possible.

Suggested Studies

The following examples are worded as null hypotheses, meaning that the statement predicts no difference between groups. If, in fact, there seems to be a difference, the null hypothesis will be rejected.

  • Regular exercise has no effect on results of cardiovascular fitness assessments (described in the Cardiovascular Fitness exercise). You can use one of those tests and compare subjects with sedentary vs. active lifestyles.
  • Blood pressure (using either the first, systolic, or the second, diastolic number) is not influenced by age, body weight, gender, etc. Example: “People in their forties have similar BPs to people in their twenties.” You can take advantage of the free monitors in drug stores and supermarkets.
  • Smoking has no effect on resting pulse. Example: “People smoking a minimum of three cigarettes a day for a minimum of two years have similar resting pulse rates to non-smokers.” You would need to be careful to keep other variables, such as age and general health, constant.
  • Athletes playing different positions are sized similarly, reflecting comparable body composition. Example: “Body Mass Indices (BMIs) are the same for NFL guards as for kickers.” BMIs can be determined from public record of heights and weights.
  • These are merely a few of countless comparisons that can be done. Your instructor will work with you to refine your hypothesis and help you set up your study. YOU ARE REQUIRED TO DISCUSS THIS STUDY WITH YOUR INSTRUCTOR BEFORE BEGINNING WORK ON IT. THERE WILL BE POINT DEDUCTIONS IF YOUR STUDY IS NOT SET UP WELL AS A RESULT OF NOT SEEKING GUIDANCE!

    General Instructions

    You will need a total of twenty subjects. Ten will be in each group.

    All twenty individuals should be as similar to each other as possible regarding variables that might mask the one being studied.

    For example, if you were interested in the relationship between age and blood pressure you would not want to include a subject who was anemic, lowering BP due to a medical condition not related to age.

    You will collect the relevant data, display results in a table, and perform a statistical test to determine if you can reject the null hypothesis.

    Lab Report

    The following are required sections to be included in your report. Grading preference will be given to concise and well-structured sentences that do not stray off topic:

    Introduction – This will include your hypothesis and how you decided upon it.

    Methods – How you selected your subjects and gathered data.

    Results – A description of your findings which includes a table displaying the data.

    Analysis – A brief description of your statistical review, using the student’s t test described below. You should report the mean (average) and sample variance for each group, as well as the calculated t value. Please show your work.

    Discussion – Does your t value allow you to accept or reject your null hypothesis? What are your thoughts regarding this?

    Conclusions – Suggest a follow up study for further investigation. What would you have done differently this time?

    Instructions for performing a student’s t test

    This statistical test will allow you to assign confidence to a statement that you make regarding your null hypothesis. You will only be able to reject the null hypothesis, suggesting that your two groups are different, if the t value that you calculate is greater than 1.833.

    In order to determine the t value you will need to calculate the mean (average) for each of your two groups. (The “x” with the bar over it is the symbol for mean.) After calculating the two means, designate the group with the higher mean as group 1. The group with the lower mean will be group 2; otherwise you can wind up with a negative t value.


    Measures of Dispersion represent how widely spread observations are relative to the mean. It’s more difficult to demonstrate difference between two groups when there is great dispersion.

    One measure of dispersion is variance. Variance is measure of how much variation exists in the population. You will need to calculate sample variance for each group to determine the t value.

    This is done by subtracting the mean from each data value and then squaring that value as depicted by the equation below. These values are then added up and divided by n-1, which will be 9.

    s2 = sample variance

    Once you know the sample variance and mean for each group you can calculate the t value:

    The numerator is the difference between the group means. The denominator is the square of the sum of the sample variances divided by n-1, which is 9.

    As you can see, the t value will be larger with a bigger difference between the group averages and with less variance within each group, making the numerator larger than the denominator.

    This means that even if the group averages are very different, if there’s also a lot of variation (scientists sometimes refer to this is “slop”) the t value will be small and the groups will not be significantly different.


    If your calculated t value is at least 1.833 you can reject the null hypothesis and you would be at least 95% certain that the two groups are different. If the t value is less than 1.833, you cannot reject the null hypothesis; the two groups are not significantly different.

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