A statistical technique called analysis of variance, or ANOVA, divides observed variance data into several components for use in further testing. When there are three or more data groups, a one-way ANOVA is used to find out how the dependent and independent variables are related.
There is a probability that you will create a Type I mistake with every t-test you run. Usually, this inaccuracy has a 5 percent rate. Running two t-tests on the same set of data increases the likelihood of "making a mistake" to 10%. It is not as straightforward as multiplying 5% by the number of tests to determine the new error rate for multiple t-tests. However, the outcomes are remarkably comparable if we merely perform a few multiple comparisons. Therefore, three t-tests would equal 15% (in actuality, 14.3%), and so on.
These mistakes must be corrected. To ensure that the Type I error stays at 5% and that any statistically significant results we get are not the consequence of simply conducting several tests, an ANOVA accounts for these mistakes.
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