![]() If there is a lot of variance (spread of data away from the mean) within the data groups, then there is more chance that the mean of a sample selected from the data will be different due to chance.Īs well as looking at variance within the data groups, ANOVA takes into account sample size (the larger the sample, the less chance there will be of picking outliers for the sample by chance) and the differences between sample means (if the means of the samples are far apart, it’s more likely that the means of the whole group will be too).Īll these elements are combined into a F value, which can then be analyzed to give a probability (p-value) of whether or not differences between your groups are statistically significant.Ī one-way ANOVA compares the effects of an independent variable (a factor that influences other things) on multiple dependent variables. ![]() It works by analyzing the levels of variance within the groups through samples taken from each of them. Like the t-test, ANOVA helps you find out whether the differences between groups of data are statistically significant. There are other variations that can be used in different situations, including:įree eBook: The guide to modern agile research How does ANOVA work? ![]() Put simply, ANOVA tells you if there are any statistical differences between the means of three or more independent groups. ![]() It’s a statistical test that was developed by Ronald Fisher in 1918 and has been in use ever since. ![]()
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