Assumptions about parametric and non parametric tests

Assumptions about parametric and non parametric tests in detail

1. Assumptions about parametric and non parametric tests Barath Kumar Babu

2. Parametric and non parametric test Parametric test: A statistical test, in which specific assumptions are made about the population parameter is known as parametric test. Non parametric test: When the word “non parametric” is used in stats, it doesn’t quite mean that you know nothing about the population. It usually means that you know the population data does not have a normal distribution

3. Assumptions about Parametric test Normality : Data have a normal distribution (or at least is symmetric) Homogeneity of variances : Data from multiple groups have the same variance Linearity : Data have a linear relationship Independence : Data are independent

4. Assumptions about Parametric test One sample, two sample and paired t-test: Population is normally distributed Sample is drawn from the population and it should be random We should know the population mean Anova test: The samples are independent and selected randomly. Parent population from which samples are taken is of normal distribution . Various treatment and environmental effects are additive in nature. The experimental errors are distributed normally with mean zero and variance. ANOVA compares variance by means of F-ratio: It again depends on experimental designs  

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