ANOVA is a parametric statistical technique that helps in finding out if there is a significant difference between the mean of three or more groups. It checks the impact of various factors by comparing groups (samples) on the basis of their respective mean.

**We can use this only when:**

- the samples have a normal distribution.
- the samples are selected at random and should be independent of one another.
- all groups have equal standard deviations.

**The assumptions and limitations of a one-way ANOVA**

- Normality – that each sample is taken from a normally distributed population
- Sample independence – that each sample has been drawn independently of the other samples
- Variance equality – that the variance of data in the different groups should be the same
- Your dependent variable – here, “weight”, should be continuous – that is, measured on a scale which can be subdivided using increments (i.e. grams, milligrams)

**The assumptions and limitations of a two-way ANOVA**

- Your dependent variable – here, “weight”, should be continuous – that is, measured on a scale which can be subdivided using increments (i.e. grams, milligrams)
- Your two independent variables – here, “month” and “sex”, should be in categorical, independent groups.
- Sample independence – that each sample has been drawn independently of the other samples
- Variance Equality – That the variance of data in the different groups should be the same
- Normality – That each sample is taken from a normally distributed population