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About Parametric Tests

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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