Having data is only half the battle. How do you know your data actually means something? With some simple Python code, you can quickly check if differences in data are actually significant. In ...

In the early 20 th century, Guinness breweries in Dublin had a policy of hiring the best graduates from Oxford and Cambridge to improve their industrial processes. At the time, it was considered a ...

Basic concepts in hypothesis testing, including effect sizes, type I and type II errors, calculation of statistical power, non-centrality parameter, and applications of these concepts to twin studies.

Advances in high-throughput biology and computer science are driving an exponential increase in the number of hypothesis tests in genomics and other scientific disciplines. Studies using current ...

Consumers of research should not be satisfied with statements that “X is effective”, or “Y has an effect”. Gwenae l Piaser Empirical science needs data. But all data are subject to random variation, ...

Learn about standard error, its role as the standard deviation of a sample, and how it measures the accuracy of a sample ...