Hypothesis testing is the use of statistics to determine the probability that a given hypothesis is true. The usual process of hypothesis testing consists of four steps.
1. Formulate the null hypothesis (commonly, that the observations are the result of pure
chance) and the alternative hypothesis (commonly, that the observations show
a real effect combined with a component of chance variation).
3. Compute the P-value, which is the probability that a test statistic at least as significant as the one observed would be obtained assuming
that the null hypothesis were true. The smaller
the -value,
the stronger the evidence against the null hypothesis.
4. Compare the -value
to an acceptable significance value (sometimes called an alpha
value). If ,
that the observed effect is statistically significant, the null hypothesis is ruled
out, and the alternative hypothesis is valid.
(commonly, that the observations are the result of pure
chance) and the alternative hypothesis 
(commonly, that the observations show
a real effect combined with a component of chance variation).
-value,
the stronger the evidence against the null hypothesis.
-value
to an acceptable significance value 
(sometimes called an alpha
value). If 
,
that the observed effect is statistically significant, the null hypothesis is ruled
out, and the alternative hypothesis is valid.
