ESDU 91017 uses a flowchart to guide its user in the application of statistical techniques to the analysis of performance data from which all but random variability has been removed, that is they have been reduced to standard conditions. After introducing the concept of hypothesis testing, the possibility of combining small samples in a larger sample, so improving the accuracy of predictions, is considered. Fisher's Variance Ratio F test is used to decide with a certain confidence whether the samples are from the same population, and Student's t test determines the significance between sample means. Larger samples, exceeding 30 points, either from combined smaller samples or as measured, are treated using Normal distribution statistics provided the Chi Squared Test for normality with a chosen confidence is met. If the test fails, a transformation of the data (for example, taking the logarithm) may provide a Normal distribution. The use of probability graph paper as a means of analysing data to the Normal distribution is explained. Bartlett's test for the consistency of variances calculated from three or more samples is introduced. Regression is considered because, although not a statistical technique, the results are analysed statistically. The manual calculation of linear two variable regression is discussed, and determination of variance about the regression line, the significance of the regression relationship, regression confidence intervals, and the correlation coefficient and its significance are treated. Multiple linear regression is briefly considered. Tables present the statistics of the Normal distribution, Student's t distribution, the Chi Squared distribution and the F distribution. Examples on the use of the methods are found in ESDU 91018 to 91020, 92021 and 92022, 93023 and 96024.