Parametric and Non-parametric test
- Non -parametric test refers that chi-square tests don’t require assumption about population parameter nor they test hypothesis.
- Parametric test includes assumptions about parameter and hypothesis about the parameter.
- Parametric means that can be measured and non-parametric means that can’t be measured.
Difference between parametric and non-parametric test
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Parametric test |
Non-parametric test |
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a) Relies on statistical distribution of data ( Normal distribution) |
a) Doesn’t depend on any distribution ( not normal distribution) |
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b) Requires assumptions for distribution for distributional characteristics of the population. |
b) Requires no assumptions |
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c) Information about population is completely known. |
c) Information about population is unknown or unavailable. |
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d) Measure of central tendency is done by mean. |
d) Measure of central tendency is done by median. |
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e) Measurement of correlation test is done by Pearson correlation test. |
e) Measurement of correlation test is done by Spearman correlation test. |
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f) Mean and SD is known |
f) Mean and SD is unknown |
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g) Data is quantitative |
g) Data is qualitative |
