What is attrition in experiments and why does it matter?

What is attrition in experiments and why does it matter?

• A. Attrition refers to participants dropping out of the study after it has begun.
• B. Attrition refers to investigators leaving the study.
• C. Attrition refers to participants dropping out before study completion; it can threaten validity.
• D. Attrition refers to missing data due to equipment failure.

Answer: (C)

Attrition is when participants drop out before the study is finished. This matters because the data you end up analyzing come from only a portion of the original sample, and if the people who drop out aren’t random, the remaining participants may no longer represent how the groups were originally formed. That can bias the estimated effects, especially if dropout rates differ across conditions or if those who leave differ systematically in ways related to the outcomes. In short, nonrandom attrition can threaten internal validity and reduce the study’s statistical power, making it harder to trust the conclusions.

Attrition is when participants drop out before the study is finished. This matters because the data you end up analyzing come from only a portion of the original sample, and if the people who drop out aren’t random, the remaining participants may no longer represent how the groups were originally formed. That can bias the estimated effects, especially if dropout rates differ across conditions or if those who leave differ systematically in ways related to the outcomes. In short, nonrandom attrition can threaten internal validity and reduce the study’s statistical power, making it harder to trust the conclusions.