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Explain why descriptive statistics alone are insufficient for the researcher to draw this conclusion about the broader population. What is the role of inferential statistics in resolving this issue?
Case context: A researcher compares the mean self-esteem scores of a sample of 50 students who participated in a mindfulness workshop with a sample of 50 students who did not. The descriptive statistics show a higher mean score in the workshop group. The researcher wants to conclude that the mindfulness workshop increases self-esteem for all college students.
Question: Explain why descriptive statistics alone are insufficient for the researcher to draw this conclusion about the broader population. What is the role of inferential statistics in resolving this issue?
Sample answer: Descriptive statistics only summarize the scores of the specific sample in the study. The observed difference in mean self-esteem scores could simply be due to random chance variability. To generalize the findings to all college students, the researcher must use inferential statistics to determine if the difference is statistically significant, meaning it is unlikely to have occurred by chance and reflects a genuine effect in the population.
Key points:
- Descriptive statistics only summarize sample data and cannot generalize on their own.
- The difference in sample means could be due to random chance variability.
- Inferential statistics are needed to determine if the results are statistically significant (unlikely to be due to chance).
- Inferential statistics allow researchers to draw conclusions about the broader population based on sample data.
Rubric: The response must explain that sample differences can occur due to random chance, identify the need for inferential statistics to test for statistical significance, and explain that this is necessary to generalize results to the population.
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Research Methods in Psychology - 4th American Edition @ KPU
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Probabilistic Nature of Statistics
Confidence Interval
Statistically Significant
Type I Error
Type II Error
Mehl's Study on Sex Differences in Talkativeness
Kanner's Study on Daily Hassles and Symptoms
Null Hypothesis Testing
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