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It has long been argued that we need to consider much more than an observed point estimate and a p-value to understand statistical results. One of the most persistent misconceptions about p-values is that they are necessarily calculated assuming a null hypothesis of no effect is true. Instead, p-values can and should be calculated for multiple hypothesized values for the effect size. For example, a p-value function allows us to visualize results continuously by examining how the p-value variesdoi:10.5451/unibas-ep89773 fatcat:aqtwb7abafcdraozosbb566gue