The resulting P values are worse than useless: counting each cell as a separate n can easily result in false-positive rates of >50% ( Aarts et al., 2015). In the case of treating each cell as an n, the assumption that is violated is independent sampling, not necessarily the null hypothesis. But a small P value does not actually tell us which assumption is incorrect, the null hypothesis or some other assumption of the statistical model (e.g., normal distribution, random sampling, equal variance, etc.). A P value reports the probability that the observed data-or any more extreme values-would occur by chance (the “null hypothesis”). The P value should be treated as a mere heuristic, interpreted as the degree of compatibility between the observed dataset and a given statistical model. ![]() ![]() While far from perfect, the P value offers a pragmatic metric to infer whether an observed difference is reproducible and substantial relative to the noise in the measurements ( Greenwald et al., 1996).
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