Explain why the t-statistic helps
Two SigmaData Scientist · Technical Screen
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This question evaluates understanding of the t-statistic, standardized effect sizes, standard errors, hypothesis testing, and interpretation of statistical evidence within the Statistics & Math domain, and it targets conceptual understanding with practical application implications.
Suppose you estimate an effect size (\hat{\beta}) in a regression model or an A/B test and compute a standard error (SE(\hat{\beta})).
Explain why the t-statistic [ t = \frac{\hat{\beta}}{SE(\hat{\beta})} ] is often a useful summary of evidence. What does it capture that the raw coefficient or mean difference does not?
Discuss how it connects to p-values and confidence intervals, what assumptions are required, and in what situations relying on the t-statistic can be misleading.
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