Reinhart points out that if a researcher runs an experiment and collects a huge sample of data then there is a high chance that they will find a statistically significant value for p when they do the analysis. This implies that:

Question

BlackTom AI · Accepted Answer

Reinhart’s point concerns the interpretation of statistical results in the context of very large samples, where even tiny effects can yield statistically significant p-values. This leads to a broader methodological insight about the nature of inference in statistics.
Option 1: 'Interpreting the results of statistical tests requires judgement' correctly captures the nuanced takeaway. With large samples, statistical significance can occur for trivial effects, so researchers must assess practical significance, effect sizes, confidence intervals, and the study design to decide what matters in the real world.
Option 2: 'It is not possible to interpret experiments with huge samples' overstates the difficulty. While interpretation is more demanding, it is not inherently impossible; rigorous judgment and additional metrics guide interpretation.
Option 3: 'Only experiments with huge samples can be interpreted.' This is false; experiments with small or moderate samples can be interpreted, though the criteria and power considerations differ from large-sample contexts.
Option 4: 'It is better to run experiments with small samples' This is not universally true. Small samples risk insufficient power and unstable estimates, while large samples introduce emphasis on distinguishing practical significance from statistical significance; the best approach depends on context and aims.
Option 5: 'p values are not useful' This overly dismisses p-values. P-values provide information about evidence against null hypotheses, but their limitations in large samples require supplemental interpretation, not complete dismissal.

ATHK1X01 Practice Quiz (Mastery quiz questions Week 2-13)

Reinhart points out that if a researcher runs an experiment and collects a huge sample of data then there is a high chance that they will find a statistically significant value for p when they do the analysis. This implies that:

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