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1.4: How Not to Do Statistics

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    Many studies are conducted and conclusions are made. However, there are occasions where the study is not conducted in the correct manner or the conclusion is not correctly made based on the data. There are many things that you should question when you read a study. There are many reasons for the study to have bias in it. Bias is where a study may have a certain slant or preference for a certain result. The following are a list of some of the questions or issues you should consider to help decide if there is bias in a study.

    One of the first issues you should ask is who funded the study. If the entity that sponsored the study stands to gain either profits or notoriety from the results, then you should question the results. It doesn’t mean that the results are wrong, but you should scrutinize them on your own to make sure they are sound. As an example if a study says that genetically modified foods are safe, and the study was funded by a company that sells genetically modified food, then one may question the validity of the study. Since the company funds the study and their profits rely on people buying their food, there may be bias.

    An experiment could have lurking or confounding variables when you cannot rule out the possibility that the observed effect is due to some other variable rather than the factor being studied. An example of this is when you give fertilizer to some plants and no fertilizer to others, but the no fertilizer plants also are placed in a location that doesn’t receive direct sunlight. You won’t know if the plants that received the fertilizer grew taller because of the fertilizer or the sunlight. Make sure you design experiments to eliminate the effects of confounding variables by controlling all the factors that you can.


    Overgeneralization is where you do a study on one group and then try to say that it will happen on all groups. An example is doing cancer treatments on rats. Just because the treatment works on rats does not mean it will work on humans. Another example is that until recently most FDA medication testing had been done on white males of a particular age. There is no way to know how the medication affects other genders, ethnic groups, age groups, and races. The new FDA guidelines stresses using individuals from different groups.

    Cause and Effect

    Cause and effect is where people decide that one variable causes the other just because the variables are related or correlated. Unless the study was done as an experiment where a variable was controlled, you cannot say that one variable caused the other. Most likely there is another variable that caused both. As an example, there is a relationship between number of drownings at the beach and ice cream sales. This does not mean that ice cream sales increasing causes people to drown. Most likely the cause for both increasing is the heat.

    Sampling Error

    This is the difference between the sample results and the true population results. This is unavoidable, and results in the fact that samples are different from each other. As an example, if you take a sample of 5 people’s height in your class, you will get 5 numbers. If you take another sample of 5 people’s heights in your class, you will likely get 5 different numbers.

    Nonsampling Error

    This is where the sample is collected poorly either through a biased sample or through error in measurements. Care should be taken to avoid this error.

    Lastly, there should be care taken in considering the difference between statistical significance versus practical significance. This is a major issue in statistics. Something could be statistically significance, which means that a statistical test shows there is evidence to show what you are trying to prove. However, in practice it doesn’t mean much or there are other issues to consider. As an example, suppose you find that a new drug for high blood pressure does reduce the blood pressure of patients. When you look at the improvement it actually doesn’t amount to a large difference. Even though statistically there is a change, it may not be worth marketing the product because it really isn’t that big of a change. Another consideration is that you find the blood pressure medication does improve a person’s blood pressure, but it has serious side effects or it costs a great deal for a prescription. In this case, it wouldn't be practical to use it. In both cases, the study is shown to be statistically significant, but practically you don’t want to use the medication. The main thing to remember in a statistical study is that the statistics is only part of the process. You also want to make sure that there is practical significance too.


    Surveys have their own areas of bias that can occur. A few of the issues with surveys are in the wording of the questions, the ordering of the questions, the manner the survey is conducted, and the response rate of the survey.

    The wording of the questions can cause hidden bias, which is where the questions are asked in a way that makes a person respond a certain way. An example is that a poll was done where people were asked if they believe that there should be an amendment to the constitution protecting a woman’s right to choose. About 60% of all people questioned said yes. Another poll was done where people were asked if they believe that there should be an amendment to the constitution protecting the life of an unborn child. About 60% of all people questioned said yes. These two questions deal with the same issue, though giving opposite results, but how the question was asked affected the outcome.

    The ordering of the question can also cause hidden bias. An example of this is if you were asked if there should be a fine for texting while driving, but proceeding that question is the question asking if you text while drive. By asking a person if they actually partake in the activity, that person now personalizes the question and that might affect how they answer the next question of creating the fine.


    Non-response is where you send out a survey but not everyone returns the survey. You can calculate the response rate by dividing the number of returns by the number of surveys sent. Most response rates are around 30-50%. A response rate less than 30% is very poor and the results of the survey are not valid. To reduce non-response, it is better to conduct the surveys in person, though these are very expensive. Phones are the next best way to conduct surveys, emails can be effective, and physical mailings are the least desirable way to conduct surveys.

    Voluntary response

    Voluntary response is where people are asked to respond via phone, email or online. The problem with these is that only people who really care about the topic are likely to call or email. These surveys are not scientific and the results from these surveys are not valid. Note: all studies involve volunteers. The difference between a voluntary response survey and a scientific study is that in a scientific study the researchers ask the individuals to be involved, while in a voluntary response survey the individuals become involved on their own choosing.

    Example \(\PageIndex{1}\): Bias in a Study

    Suppose a mathematics department at a community college would like to assess whether computer-based homework improves students’ test scores. They use computer-based homework in one classroom with one teacher and use traditional paper and pencil homework in a different classroom with a different teacher. The students using the computer-based homework had higher test scores. What is wrong with this experiment?


    Since there were different teachers, you do not know if the better test scores are because of the teacher or the computer-based homework. A better design would be have the same teacher teach both classes. The control group would utilize traditional paper and pencil homework and the treatment group would utilize the computer-based homework. Both classes would have the same teacher, and the students would be split between the two classes randomly. The only difference between the two groups should be the homework method. Of course, there is still variability between the students, but utilizing the same teacher will reduce any other confounding variables.

    Example \(\PageIndex{2}\): Cause and Effect

    Determine if the one variable did cause the change in the other variable.

    1. Cinnamon was giving to a group of people who have diabetes, and then their blood glucose levels were measured a time period later. All other factors for each person were kept the same. Their glucose levels went down. Did the cinnamon cause the reduction?
    2. There is a link between spray on tanning products and lung cancer. Does that mean that spray on tanning products cause lung cancer?


    1. Since this was a study where the use of cinnamon was controlled, and all other factors were kept constant from person to person, then any changes in glucose levels can be attributed to the use of cinnamon
    2. Since there is only a link, and not a study controlling the use of the tanning spray, then you cannot say that increased use causes lung cancer. You can say that there is a link, and that there could be a cause, but you cannot say for sure that the spray causes the cancer.

    Example \(\PageIndex{3}\): Generalization

    1. A researcher conducts a study on the use of ibuprofen on humans and finds that it is safe. Does that mean that all species can use ibuprofen?
    2. Aspirin has been used for years to bring down fevers in humans. Originally it was tested on white males between the ages of 25 and 40 and found to be safe. Is it safe to give to everyone?


    1. No. Just because a drug is safe to use on one species doesn’t mean it is safe to use for all species. In fact, ibuprofen is toxic to cats.
    2. No. Just because one age group can use it doesn’t mean it is safe to use for all age groups. In fact, there has been a link between giving a child under the age of 19 aspirin when they have a fever and Reye’s syndrome.


    Exercise \(\PageIndex{1}\)

    1. Suppose there is a study where a researcher conducts an experiment to show that deep breathing exercises helps to lower blood pressure. The researcher takes two groups of people and has one group to perform deep breathing exercises and a series of aerobic exercises every day and the other group was asked to refrain from any exercises. The researcher found that the group performing the deep breathing exercises and the aerobic exercises had lower blood pressure. Discuss any issue with this study.
    2. Suppose a car dealership offers a low interest rate and a longer payoff period to customers or a high interest rate and a shorter payoff period to customers, and most customers choose the low interest rate and longer payoff period, does that mean that most customers want a lower interest rate? Explain.
    3. Over the years it has been said that coffee is bad for you. When looking at the studies that have shown that coffee is linked to poor health, you will see that people who tend to drink coffee don’t sleep much, tend to smoke, don’t eat healthy, and tend to not exercise. Can you say that the coffee is the reason for the poor health or is there a lurking variable that is the actual cause? Explain.
    4. When researchers were trying to figure out what caused polio, they saw a connection between ice cream sales and polio. As ice cream sales increased so did the incident of polio. Does that mean that eating ice cream causes polio? Explain your answer.
    5. There is a positive correlation between having a discussion of gun control, which usually occur after a mass shooting, and the sale of guns. Does that mean that the discussion of gun control increases the likelihood that people will buy more guns? Explain.
    6. There is a study that shows that people who are obese have a vitamin D deficiency. Does that mean that obesity causes a deficiency in vitamin D? Explain.
    7. A study was conducted that shows that polytetrafluoroethylene (PFOA) (Teflon is made from this chemical) has an increase risk of tumors in lab mice. Does that mean that PFOA’s have an increased risk of tumors in humans? Explain.
    8. Suppose a telephone poll is conducted by contacting U.S. citizens via landlines about their view of gay marriage. Suppose over 50% of those called do not support gay marriage. Does that mean that you can say over 50% of all people in the U.S. do not support gay marriage? Explain.
    9. Suppose that it can be shown to be statistically significant that a smaller percentage of the people are satisfied with your business. The percentage before was 87% and is now 85%. Do you change how you conduct business? Explain?
    10. You are testing a new drug for weight loss. You find that the drug does in fact statistically show a weight loss. Do you market the new drug? Why or why not?
    11. There was an online poll conducted about whether the mayor of Auckland, New Zealand, should resign due to an affair. The majority of people participating said he should. Should the mayor resign due to the results of this poll? Explain.
    12. An online poll showed that the majority of Americans believe that the government covered up events of 9/11. Does that really mean that most Americans believe this? Explain.
    13. A survey was conducted at a college asking all employees if they were satisfied with the level of security provided by the security department. Discuss how the results of this question could be biased.
    14. An employee survey says, “Employees at this institution are very satisfied with working here. Please rate your satisfaction with the institution.” Discuss how this question could create bias.
    15. A survey has a question that says, “Most people are afraid that they will lose their house due to economic collapse. Choose what you think is the biggest issue facing the nation today.
      1. Economic collapse
      2. Foreign policy issues
      3. Environmental concerns.” Discuss how this question could create bias.
    16. A survey says, “Please rate the career of Roberto Clemente, one of the best right field baseball players in the world.” Discuss how this question could create bias.

    See solutions

    This page titled 1.4: How Not to Do Statistics is shared under a CC BY-SA 4.0 license and was authored, remixed, and/or curated by Kathryn Kozak via source content that was edited to the style and standards of the LibreTexts platform.