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Data Analysis is a huge field and many people feel confused regarding the kind of statistical test that should be undertaken for analyzing their data.

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There are two kinds of statistical methodologies or tests used for analyzing data- parametric tests and non-parametric tests. Parametric tests are hugely popular and they are considered a powerful statistical test.

Parametric tests make assumptions regarding data. The data is drawn from a population with a normal or specific distribution. In a parametric test, the variables in a population are measured depending on the interval scale. Parametric tests are taken when data have a normal distribution and when a measurement scale is a ratio or interval.

### What are the Kinds of Parametric Tests?

The following are the kinds of parametric tests that are discussed in our Parametric assignment help desk.

1. t-test , where n < 30. Ut is classified further into sample 1 and sample 2.
2. Pearson’s Correlation
3. ANOVA or Analysis of Variance
4. Z-test for samples > 30

Student T-Test

The test was developed in 1908 by Prof. W.S. Gossett who published statistical papers under the name ‘Student.’ This is the reason why this test is called the Student t-test. This test is taken when the samples are small and when the population variances are unknown.

The assumptions

• The samples are selected randomly
• The variables follow a normal distribution
• Data is quantitative
• Samples are very small, lower than 30
• Sample variance is the same in both the groups

The uses

• It is used to compare means of independent samples
• It can compare two proportions of the independent samples
• It can compare population mean and sample mean

### ANOVA or Analysis of Variance

The principle objective is to explain the variations in measurements. When there are more than two groups, ANOVA statistical model is used.

Assumptions:

• Sample population can be approximated easily to normal distribution
• Independent samples
• The populations have the same Standard Deviation
• The population is selected randomly.

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### Pearson’s Correlation Coefficient

Pearson’s Correlation Coefficient measures the strength of an association between two variables.

Assumptions

• The subjects chosen for study along with a pair of values X and Y are selected with a random sampling method.
• X and Y variables are constant
• X and Y varaibles follow the normal distribution

Kinds of Correlation

Perfect Positive Correlation r=+1

Perfect negative correlation r=-1

Partial Positive Correlation 0<r<+1

Partial negative correlation 0>r>-1

No correlation r=0

Z-test

This test is used to test major differences between two means n>30.

Assumptions

• Data should be quantitative
• The sample should be selected randomly
• The samples must be more than 30
• Data must follow a normal distribution
• Sample variances must be the same in both groups under study.

All tests are discussed in our Parametric homework help online.

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