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Explain why the researcher's conclusion about the importance and power of the study aid is misleading. In your explanation, describe how the sample size of 15,000 students affected the -value, and explain what Cohen's reveals about the actual magnitude of the relationship.
Case context: A researcher is studying the impact of a new study aid on exam scores. Using a very large sample of 15,000 students, the researcher finds a statistically significant difference between the group that used the study aid and the control group (). However, the calculated effect size is Cohen's . The researcher concludes that the study aid is an incredibly powerful and important tool for all students because of the highly significant -value.
Question: Explain why the researcher's conclusion about the importance and power of the study aid is misleading. In your explanation, describe how the sample size of 15,000 students affected the -value, and explain what Cohen's reveals about the actual magnitude of the relationship.
Sample answer: The researcher's conclusion is misleading because they are equating statistical significance with practical importance. The extremely large sample size of 15,000 students makes the null hypothesis test highly sensitive, meaning even a very weak relationship can produce a highly significant -value (). The effect size of Cohen's estimates the actual strength of the relationship in the population, showing that the magnitude of the difference is extremely small and has little practical importance.
Key points:
- A -value alone only indicates statistical significance and is heavily influenced by sample size.
- A large sample size can make a very weak relationship statistically significant.
- Effect size estimates the actual strength of a statistical relationship in the population.
- An effect size of Cohen's indicates an extremely weak relationship magnitude.
- Statistical significance does not automatically imply that a relationship is strong or practically important.
Rubric: A successful answer must demonstrate comprehension by: 1. Diagnosing that the researcher incorrectly equated a low -value with a strong relationship. 2. Explaining that the large sample size () is the reason the -value is statistically significant. 3. Justifying that the small effect size () indicates the actual magnitude/strength of the relationship is negligible.
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Research Methods in Psychology - 4th American Edition @ KPU
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Effect Size in Studies Lacking Power
Correlation Coefficient
Cohen's
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