The difference in the two sample means obtained by Jennie and Charles can be attributed to the selection of players they sampled.
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Jennie's Sample Mean:
- Jennie sampled three players and calculated a mean score of 14. This indicates that the players she selected likely scored lower points compared to the overall distribution of scores. For instance, it's plausible that she sampled players with scores close to or below the lower scores in the dataset (such as 12, 18, 8, or 14).
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Charles's Sample Mean:
- Charles, on the other hand, sampled three players and arrived at a mean score of 54. This suggests that the players he chose scored significantly higher points. It’s possible that his sample included players with the highest scores in the dataset, such as 56, 80, and 26, which would significantly elevate the sample mean.
In summary, the difference in the sample means (14 vs. 54) can be explained by the fact that Jennie selected players with lower scores, while Charles selected players with higher scores. This highlights how sampling can influence statistical outcomes, emphasizing the importance of sample selection in drawing conclusions from data.