What is ANOVA and its assumptions?

Data Science, AI and ML
#1

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