Statistical significance is always practically important.

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BlackTom AI · Accepted Answer

Question restatement: The statement to evaluate is 'Statistical significance is always practically important.'

Option analysis:
- Correct option: False. The claim that statistical significance is always practically important is not accurate. Statistical significance only tells us that the observed effect is unlikely to be due to random chance under a null hypothesis, given the sample size and variability. It does not indicate how large or meaningful the effect is in real-world terms.

Why False in detail:
- Statistical significance is influenced by sample size. With very large samples, tiny differences can achieve statistical significance even when the practical impact is negligible. This shows that significance does not automatically translate to practical importance.
- Practical significance concerns effect size and real-world relevance. An effect can be statistically significant but have a trivial or unimportant practical effect, such as a minuscule change in a measure that does not matter in practice.
- Context matters. The same statistically significant result may be valuable in one domain (e.g., medical treatment with meaningful health benefits) and irrelevant in another (e.g., detecting a tiny difference in a metric that has no cost or benefit implications).
- Confidence intervals and effect sizes provide better insight into practical significance than p-values alone. A narrow confidence interval around a small effect may still be statistically significant but not practically meaningful; conversely, a large effect with borderline significance in a small sample may be practically important.
- Misinterpretations to avoid: assuming significance proves importance, assuming non-significance means no effect, or ignoring study design limitations that affect real-world applicability.

In summary, statistical significance addresses the probability that an observed effect is not due to chance under a specific model, whereas practical significance requires evaluating the magnitude, relevance, and context of the effect. Therefore, the statement is not universally true and is appropriately classified as false.

ECON 3640-090 Spring 2025 Lecture #19 Warm-Up

Statistical significance is always practically important.

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