Glossary of statistical terms

Hypothesis Testing:

Hypothesis testing (also called "significance testing") is a statistical procedure for discriminating between two statistical hypotheses - the null hypothesis (H0) and the alternative hypothesis ( Ha, often denoted as H1). Hypothesis testing, in a formal logic sense, rests on the presumption of validity of the null hypothesis - that is, the null hypothesis is rejected only if the data at hand testify strongly enough against it.

The philosophical basis for hypothesis testing lies in the fact that random variation pervades all aspects of life, and in the desire to avoid being fooled by what might be chance variation. The alternative hypothesis typically describes some change or effect that you expect or hope to see confirmed by data. For example, new drug A works better than standard drug B. Or the accuracy of a new weapon targeting system is better than historical standards. The null hypothesis embodies the presumption that nothing has changed, or that there is no difference.

Hypothesis testing comes into play if the observed data do, in fact, suggest that the alternative hypothesis is true (the new drug produces better survival times than the old one in an experiment, for example). We ask the question "is it possible that chance variation might have produced this result?"

As noted, the null hypothesis stands ("is not rejected") unless the data at hand provide strong enough evidence against it. "Strong enough" means that the probability that you would obtain a result as extreme as the observed result, given that the null hypothesis is true, is small enough (usually < 0.05) given the null hypothesis is true.

Browse Other Glossary Entries

Statistics.com offers over 100 courses in statistics from introductory to advanced level. Most are 4 weeks long and take place online in series of weekly lessons and assignments, requiring about 15 hours/week. Participate at your convenience; there are no set times when you must to be online. Ask questions and exchange comments with the instructor and other students on a private discussion board throughout the course.

Statistics 1 – Probability and Study Design

This course, the first of a 3-course sequence, provides an introduction to statistics for those with little or no prior exposure to basic probability and statistics.  It runs every eight weeks.

Statistics 2 – Inference and Association

The aim of this course is to provide an easy introduction to inference and association through a series of practical applications, based on the resampling/simulation approach. Once you have completed this course you will be able to test hypotheses and compute confidence intervals regarding proportions or means, computer correlations and fit simple linear regressions.

Survey Analysis

This course covers the analysis of data gathered in surveys.

Prediction & Tolerance Intervals; Measurement and Reliability

The topics covered in this course include prediction intervals, tolerance intervals, calibration intervals, measurement error, accelerated life testing, measurement system appraisal, reliability and lifetime testing.

Back to Main Glossary

Promoting better understanding of statistics throughout the world

To celebrate the International Year of Statistics in 2013, we started a program to provide a statistical term every week, delivered directly to your inbox. The Word of the Week program proved to be quite popular, and continues. The Institute for Statistics Education offers an extensive glossary of statistical terms, available to all for reference and research. Make it your New Year's resolution to improve your own statistical knowledge! Sign up here. Rather not have more email? Simply bookmark our home page and check our “Stats Word of the Week” feature.

Want to be notified of future courses?

Yes