Observed proportion = theoretical proportionĬompare the proportion of a female group to a proportion of 0.5 in a sample McNemar's test (for 2 series) Cochran's Q test (for more than 2 series)Ĭompare 2 variances (could be used to test assumption 3)Ĭompare the natural dispersion of size in 2 different varieties of a fruitĬompare several variances (could be used to test assumption 3)Ĭompare the natural dispersion of size in several different varieties of a fruitĬompare an observed proportion to a theoretical oneġ observed proportion with its associated sample size, one theoretical proportion Several series of binary measurements on the same unitsĪ group of assessors (units) evaluate the presence/absence of an attribute in a group of products Repeated measures Analysis of Variance (ANOVA), mixed modelsįriedman's test for complete block designs Durbin, Skillings-Mack's test for incomplete block designs Page test for cases where series scores are expected to increase or to decrease (across time for example)Ĭompare series of binary data (dependent measurements) Several series of quantitative measurements on the same unitsįollow the concentration of a trace element in a group of plants across time Two series of quantitative measurements on the same units (before-after…)Ĭompare the mean hemoglobin concentration before and after a treatment has been applied on a group of patientsĬompare several observed means (dependent measurements) Measurements on one sample and 1 theoretical mean (1 number)Ĭompare an observed pollution rate to a standard valueĬompare two observed means (independent samples)Ĭompare hemoglobin concentration between two groups of patientsĬheck if the effect of medication A is the same as the effect of medication B on the concentration of a molecule in miceĬompare several observed means (independent samples)Ĭompare corn yields according to 4 different fertilizersĬompare two observed means (dependent measurements) Test familyĬonditions of validity (parametric tests)Ĭompare an observed mean to a theoretical one Please notice that the list is not exhaustive, and that many other situations / tests exist. The displayed tests are the most commonly used tests in statistics. How to interpret the output of a statistical test: the significance level alpha and the p-value What is the difference between a two-tailed and a one-tailed test? What is the difference between a parametric and a nonparametric test? What is a difference between paired and independent samples tests? In some situations, parametric tests do not exist and so only nonparametric solutions are proposed.įor a more theoretical background on statistical testing, please read the below articles: When available, nonparametric equivalents are proposed. Conditions of validity of parametric tests are listed in the paragraph following the grid. In columns Parametric tests and Nonparametric tests, you may click on the link to view a detailed tutorial related to the proposed test including a data file. The guide proposes a formulation of the null hypothesis, as well as a concrete example in each situation. We have drawn the grid below to guide you through the choice of an appropriate statistical test according to your question, the type of your variables (i.e., categorical variables, binary, continuous) and the distribution of data. XLSTAT provides a high number of statistical tests. A guide to choosing an appropriate test according to the situation Usually, H0 is a statement of equality (equality between averages or between variances or between a correlation coefficient and zero, for example). In other words, the controlled processes (the experimental manipulations for example) do not affect the data. Under H0, data are generated by random processes. This hypothesis is called the null hypothesis and is often referred to as H0. What is a statistical test?Ī statistical test is a way to evaluate the evidence the data provides against a hypothesis. This article will help you choose the right statistical test for your data.
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