parametric versus non parametric test

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Parametric VERSUS Non Parametric test BY JWANIKA VANSIYA

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Parametric VERSUS

Non Parametric test

BY JWANIKA VANSIYA

Parametric test

• A statistical test, in which specific assumptions are made about the population parameter is known as parametric test.

Parametric test

When to use parametric test?

Four conditions have to be satisfied:

Data must be interval/ ratio

Subjects should be randomly selected

When to use parametric test ????

.Data should be normally distributed.

.

Variation in the results should be roughly same..• Homogenecity of variances assessed by

Levene’s test

Non parametric test

• Nonparametric tests are also called distribution-free tests because they don’t assume that your data follow a specific distribution.

Non parametric test

When to use non parametric test?

• Nominal or ordinal data

1

• Non normal distribution of data2

Parametric test or Non parametric test-Determination

Parametric versus Non Parametric test.

Parametric test

Specific assumptions are made regarding the population

Parametric test is powerful if it is exists

Test statistics based on distribution

Non parametric test

No specific assumptions are made regarding the population

Not powerful like parametric test

Test statistics is arbitrary

.Parametric test

No parametric test exists for nominal scale data

Central measure - mean

Can draw more conclusions

Non parametric test

Non parametric test exists for nominal scale data

Central measure - median

Simplicity , not affected by outliers

Parametric versus non parametric testStudy type Parametric test Non parametric testCompare means between two distinct/independent groups

Two-sample t-test Mann- whitney test

Compare two quantitative measurements taken from the same individual

Paired t-test Wilcoxon signed-rank test

Compare means between three or more distinct/independent groups

Analysis of variance (ANOVA)

Kruskal-Wallis test

Study type Parametric test Non parametric test

Repeated measures, >2 conditions

One-way, repeated measures ANOVA Friedman's test

Estimate the degree of association between two

quantitative variables Pearson coefficient of correlation

Spearman’s rank correlation

Thank you