Clinical research typically gathers sample data to make an inference about a population. Sample data carries the risk of introducing variation into the data, which can be estimated by the standard error of the mean. Data are described using descriptive statistics such as mean, median, mode, and standard deviation. The strength of the relation between two groups of data can be described using correlation. Hypothesis testing allows the researcher to accept or reject a null hypothesis by calculating the probability that differences between groups are the result of chance. By convention, if the probability is less than .05, the difference between the groups is said to be statistically significant. This probability is determined by statistical tests. Of these groups of tests, the Student t test and the analysis of variance are the more common parametric tests, and the chi-square test is common for nonparametric tests. This article provides a basic overview of biostatistics to assist the nonstatistician with interpreting statistical analyses in research articles.
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