Statistical/Hypothesis testing is a common method of drawing inferences about a population based on statistical evidence from a sample.
The p value of a test is the probability, under the null hypothesis, of obtaining a value of the test statistic as extreme or more extreme than the value computed from the sample.
The significance level of a test is a threshold of probability agreed to before the test is conducted. A typical value of α is 5%. If the p value of a test is less than , the test rejects the null hypothesis. If the p value is greater than , there is insufficient evidence to reject the null hypothesis. Note that lack of evidence for rejecting the null hypothesis is not evidence for accepting the null hypothesis. Also note that substantive "significance" of an alternative cannot be inferred from the statistical significance of a test.
Results of hypothesis tests are often communicated with a confidence interval. A confidence interval is an estimated range of values with a specified probability of containing the true population value of a parameter. Upper and lower bounds for confidence intervals are computed from the sample estimate of the parameter and the known (or assumed) sampling distribution of the estimator.