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Chapter 1: Problem 47

Indicate whether we should trust the results of the study. Is the method of data collection biased? If it is, explain why. Ask a random sample of students at the library on a Friday night "How many hours a week do you study?" to collect data to estimate the average number of hours a week that all college students study.

Short Answer

Expert verified
The results of the study shouldn't be trusted. The data collection method is biased because the sample group is not representative of all college students as it's likely that individuals studying at the library on a Friday night study more often than the general college population. This bias could lead to an overestimate of the average number of study hours for all college students.

Step by step solution

01

Evaluating Selection of Sample

Consider who is being surveyed in this study - students at the library on a Friday night. This is not a true random sample of all college students because it's likely that students who are at the library on a Friday night are those who study more than average.
02

Considering the Time and Location of Questionnaire

The location and timing of the study might affect the responses. College students who are studying at the library on a Friday night might not represent all college students at that particular college/university and hence are not the best group to survey if one wants to generalize the results to all students.
03

Understanding Bias

Bias in data collection can occur when the collection method systematically favors certain outcomes. In this case, choosing to survey students who are studying at the library on a Friday night could lead to an overestimation of average study hours as this group is more likely to study more than the average student. This selection bias results in an unrepresentative sample, causing the study results to be untrustworthy.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Data Collection Methods
When gathering data, choosing the appropriate method is crucial to obtain reliable and accurate results. There are many ways data can be collected, and the method chosen can significantly affect the outcomes.
  • Surveys: These involve asking people questions directly, either in person, via phone, or online. It's important to form questions that are clear and unbiased.

  • Observations: This method involves watching and recording behaviors or outcomes without directly questioning participants.

  • Experiments: Involve manipulating variables in a controlled setting to determine cause-and-effect relationships.

Data collection methods must be chosen carefully to avoid bias and ensure that the sample accurately reflects the greater population. Proper planning and execution can minimize errors, leading to more trustworthy analyses.
Survey Bias
Survey bias occurs when the responses obtained are systematically skewed by the method of data collection. This can happen for a variety of reasons, such as the timing of the survey or the demographic being questioned. To minimize bias, consider the following:
  • Question Wording: Questions should be neutral and not lead respondents toward a particular answer.

  • Sampling Bias: Ensure the sample is not skewed towards a particular group by avoiding specific locations or times that could result in a biased sample.

  • Non-Response Bias: Account for individuals who choose not to respond, as their lack of involvement could influence the results.
In the given exercise, asking students only at a library on a Friday night can lead to survey bias because these students are more likely to be studying. To avoid this, a broader sampling across different times and locations is recommended.
Representative Sample
A representative sample accurately reflects the characteristics of the entire population. Achieving this is key in any data collection because it ensures that findings can be generalized to the broader group. Here are the steps to ensure a sample is representative:
  • Random Sampling: Every individual in the population should have an equal chance of being selected.

  • Diversity: The sample should include a variety of individuals from different backgrounds, locations, and times.

  • Sample Size: A larger sample often provides a more accurate representation by reducing sampling error.
In the exercise scenario, simply choosing students at the library cannot provide a representative sample of all college students as it likely reflects only those who study more. Instead, sampling from different environments and times would offer a more comprehensive view of all student study habits.

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Most popular questions from this chapter

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