Who was student and why do we care so much about his t-test?

Statistics is the study of populations, how they relate to one another and what effect sampling has in terms of representing the original population. Use of statistical tools has been facilitated by modern computer technology; however, given the ease in which results are obtained, it is easy to overlook potentially incorrect use of the various statistical tests. Statistical tools are invaluable for investigators because they make it possible to determine if scientific results are important. When a test is used, it is important to know that the result of any statistical test is valid to avoid erroneous conclusions. To do so, the investigator must have a basic understanding of the test's assumptions and limitations. Modern statistical packages often include a variety of results relating to the test's applicability to the data analyzed. Not uncommonly, biologists are unfamiliar with these analyses. This review intends to improve the reader's understanding of t-tests by providing a history of Student's t-test, and its assumptions, applications, and limitations. Part 1 of the series ("The Mean and Standard Deviation: What Does It All Mean?") reviewed basic aspects of distributions, measures of central tendency, and dispersion assessment. Small sample size effects on accurate estimation of a population mean and group comparisons for continuous data are presented in this review.