The null hypothesis is the noise; the alternative hypothesis is the signal. The size of the effect is like the sensitivity, and it determines how easy it is to tell signal from noise. The scientist needs to apply some criterion or response bias before breaking out the champagne, called the critical value: below the critical value, she fails to reject the null hypothesis and drowns her sorrows; above the critical value, she rejects it and celebrates—she declares the effect to be “statistically significant.”
