Alternative to the statistical mass confusion of testing for "no effect"

It should not be controversial to argue that the proximate goal of measuring something is to figure out how big or small or fast or slow it is. Estimates of effect size can be used to build models of how cells work and to test quantitative predictions. Unfortunately, in cell biology, quantification is nearly synonymous with null-hypothesis significance testing. The hypothesis being tested is universally assumed to be the hypothesis that there was no effect. Framing every experiment as an attempt to reject the no-effect hypothesis is convenient but doesn't teach us about cells. In this manuscript, I walk through some of the common critiques of significance testing and how these critiques relate to experimental cell biology. I argue that careful consideration of effect size should be returned to its central position in the planning and discussion of cell biological research. To facilitate this shift in focus, I recommend replacing p-values with confidence intervals as cell biology's default statistical analysis.