A teacher is interested in finding out how many hours a day a college student at

Monroe Community College exercises. The teacher randomly chooses 1000 college
students and asks them, “How many hours do you exercise?” Explain whether there
could be any bias in the data the teacher collects

The teacher did not define exercise. The teacher also did not indicate whether the exercise is daily, weekly, or monthly.

Bias refers to any systemic error in data collection that leads to a distortion or deviation from the true value. In this case, there could be potential bias in the data collected by the teacher when estimating the number of hours a day a college student at Monroe Community College exercises. Here's how bias could occur:

1. Sample Bias: The teacher randomly chooses 1000 college students. However, if the selection of students is not truly random, and certain groups of students are overrepresented or underrepresented, the data might not be representative of the entire student population. For example, if the teacher only selects students from certain majors or specific clubs, the data may not reflect the exercise habits of all students at Monroe Community College.

2. Non-response Bias: If some students choose not to respond to the teacher's question or refuse to answer honestly, this could introduce bias. Non-response bias occurs when the characteristics of those who respond differ from those who do not respond. For instance, if students who exercise more are more likely to respond, the data will be skewed towards higher exercise levels.

3. Social Desirability Bias: Social desirability bias refers to individuals providing answers that they perceive as socially acceptable rather than the truth. In this case, students may feel pressured to provide answers that make them appear healthier or more dedicated to exercise, leading to an overestimation of the exercise hours reported.

4. Recall Bias: When students are asked to recall their exercise habits, they may not accurately remember or estimate their exercise hours. Memory limitations or subjective interpretations can introduce bias in the data, resulting in underestimation or overestimation of exercise hours.

To minimize bias, the teacher could consider using techniques like stratified random sampling to ensure representation from different groups, ensuring anonymity to encourage honest responses, using clear and specific questions to reduce ambiguity, and cross-checking self-reported data with objective measures such as activity trackers or exercise logs.