The null hypothesis for an ANOVA is that all treatments/samples come from populations with the same mean. The alternative hypothesis is best stated as:

At least one of the population means is different from the others.

Step-by-step explanation:

ANOVA is a short term or an acronym for analysis of variance which was developed by the notable statistician Ronald Fisher. The analysis of variance (ANOVA) is typically a collection of statistical models with their respective estimation procedures used for the analysis of the difference between the group of means found in a sample. Simply stated, ANOVA helps to ensure we have a balanced data by splitting the observed variability of a data set into random and systematic factors.

In Statistics, the random factors doesn't have any significant impact on the data set but the systematic factors does have an influence.

Basically, the analysis of variance (ANOVA) procedure is typically used as a statistical tool to determine whether or not the mean of two or more populations are equal through the use of null hypothesis or a F-test.

Hence, the null hypothesis for an ANOVA is that all treatments or samples come from populations with the same mean. The alternative hypothesis is best stated as at least one of the population means is different from the others.

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step-by-step explanation: