In hypothesis testing, a null hypothesis (typically that there is no effect) is compared with an alternative hypothesis (typically that there is an effect, or that there is an effect of a particular sign). For example, in evaluating whether a new cancer remedy works, the null hypothesis typically would be that the remedy does not work, while the alternative hypothesis would be that the remedy does work. When the data are sufficiently improbable under the assumption that the null hypothesis is true, the null hypothesis is rejected in favor of the alternative hypothesis. (This does not imply that the data are probable under the assumption that the alternative hypothesis is true, nor that the null hypothesis is false, nor that the alternative hypothesis is true. Confused? Take a course in Statistics!)