An insurance company wants to know if the average speed at which men drive cars is higher than that of women drivers. Which of the following is NOT a possible confounding variable and choice to address that variable?

MATLAB: An Introduction with Applications
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Author:Amos Gilat
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### Understanding Confounding Variables in Speed Comparison Research

**Context:**
An insurance company is investigating whether the average speed at which men drive cars is higher than that of women drivers. 

**Question:**
Which of the following is NOT a possible confounding variable, and what measures should be taken to address that variable?

**Options:**
1. **Road conditions, so the speeds should be measured at the same sites.**
   - To eliminate road conditions as a confounding variable, data collection should occur at the same locations where the road conditions are consistent for both male and female drivers.

2. **Make of car, so the researchers should pick one make and only measure that.**
   - Controlling for the make of the car can help in ensuring that any differences in driving speed are not due to different car performances. The researchers should standardize the car make across the sample group.

3. **Amount of traffic, so the speeds should be measured at the same time of day.**
   - The amount of traffic can significantly influence driving speed. Therefore, measuring the speeds at the same time of day can ensure that both male and female drivers are experiencing similar traffic conditions.

4. **Type of driver, so commercial vehicles should be excluded from the study.**
   - Excluding commercial vehicles ensures that the sample consists solely of personal vehicle drivers, who may have different driving patterns than commercial drivers.

**Detailed Explanation:**

Out of the provided options, each variable represents a potential factor that could impact the driving speed. Thus, to study the specific difference between the speeds of male and female drivers accurately, the researchers must control for these variables by standardizing or excluding them.

One of the provided choices is NOT considered as a confounding variable in this scenario, and correctly identifying this helps the researchers to focus on the relevant factors impacting their analysis.
Transcribed Image Text:### Understanding Confounding Variables in Speed Comparison Research **Context:** An insurance company is investigating whether the average speed at which men drive cars is higher than that of women drivers. **Question:** Which of the following is NOT a possible confounding variable, and what measures should be taken to address that variable? **Options:** 1. **Road conditions, so the speeds should be measured at the same sites.** - To eliminate road conditions as a confounding variable, data collection should occur at the same locations where the road conditions are consistent for both male and female drivers. 2. **Make of car, so the researchers should pick one make and only measure that.** - Controlling for the make of the car can help in ensuring that any differences in driving speed are not due to different car performances. The researchers should standardize the car make across the sample group. 3. **Amount of traffic, so the speeds should be measured at the same time of day.** - The amount of traffic can significantly influence driving speed. Therefore, measuring the speeds at the same time of day can ensure that both male and female drivers are experiencing similar traffic conditions. 4. **Type of driver, so commercial vehicles should be excluded from the study.** - Excluding commercial vehicles ensures that the sample consists solely of personal vehicle drivers, who may have different driving patterns than commercial drivers. **Detailed Explanation:** Out of the provided options, each variable represents a potential factor that could impact the driving speed. Thus, to study the specific difference between the speeds of male and female drivers accurately, the researchers must control for these variables by standardizing or excluding them. One of the provided choices is NOT considered as a confounding variable in this scenario, and correctly identifying this helps the researchers to focus on the relevant factors impacting their analysis.
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