Prefer Student 2's paired analysis, because the observational units are matched pairs: two shoes exposed to the same child's activity. A paired t-test uses , so shared child-to-child variation is removed before estimating the standard error. The independent units are the ten pairs, giving statistical degrees of freedom under the model of independent normally distributed pair differences.
The role of covariance is explicit:With common marginal variance and within-pair correlation coefficient , the true variance of the average difference is . The independent-groups calculation ignores the covariance term and uses . Positive within-pair association can therefore make the paired standard error much smaller, explaining how the reported p-values can differ without arithmetic mistakes. The preference follows from the paired design, not from selecting the smaller p-value. Marginal normality alone need not imply normally distributed differences; exact paired inference uses the normal-difference assumption, for example justified by a joint multivariate normal distribution.
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