forecasting for example, there is no agreement on a measure of forecast accuracy. In the absence of a consensus measurement, no decision based on measurements will be without controversy. Publication bias: Statistically nonsignificant results may be less likely to be published, which can bias the literature. Multiple testing: When multiple true null hypothesis tests are conducted at once without adjustment, the overall probability of Type I error is higher than the nominal alpha level. Those making critical decisions based on the results of a hypothesis test are prudent to look at the details rather than the conclusion alone. In the physical sciences most results are fully accepted only when independently confirmed. The general advice concerning statistics is, "Figures never lie, but liars figure" (anonymous). Definition of terms The following definitions are mainly based on the exposition in the book by Lehmann and Romano: Statistical hypothesis: A statement about the parameters describing a population (not a sample). Test statistic: A value calculated from a sample without any unknown parameters, often to summarize the sample for comparison purposes. : Any hypothesis which specifies the population distribution completely. Composite hypothesis: Any hypothesis which does not specify the population distribution completely. Null hypothesis (H0) Positive data: Data that enable the investigator to reject a null hypothesis. Alternative hypothesis (H1) Region of rejection / Critical region: The set of values of the test statistic for which the null hypothesis is rejected. Critical value Power of a test (1 − β) Size: For simple hypotheses, this is the test's probability of incorrectly rejecting the null hypothesis. The false positive rate. For composite hypotheses this is the supremum of the probability of rejecting the null hypothesis over all cases covered by the null hypothesis. The complement of the false positive rate is termed specificity in biostatistics. ("This is a specific test. Because the result is positive, we can confidently say that the patient has the condition.") See sensitivity and specificity and Type I and type II errors for exhaustive definitions. Significance level of a test (α) p-value Statistical significance test: A predecessor to the statistical hypothesis test (see the Origins section). An experimental result was said to be statistically significant if a sample was sufficiently inconsistent with the (null) hypothesis. This was variously considered common sense, a pragmatic heuristic for identifying meaningful experimental results, a convention establishing a threshold of statistical evidence or a method for drawing