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The test is used to compare sample average with paired data.

Introduction

Pairing allows the risk of misinterpretation to be ruled out in cases where data have common relationships that may affect the results of the tests. In this specific case, the student test allows:

  • Compare the average of 2 samples
  • Compare the variance of 2 samples (where for independent data we used the Fisher test)

 

Example :

  1. One wants to test a ” before “-” after ” on meat consumption over a year of the same individual sample. Pairing is done by the individual whose characteristics can evolve between Test 1 and test 2.
  2. We want to test whether a training plan has been effective. Pre-and post-training skills will be compared, with the pairing being performed by the individuals who have taken the training.

The principle

The principle of the test is based on the creation of the data pairs. This is the difference between the values of this data pair that we will study in the case of a comparison of averages.

For the comparison of variances, we will perform the test from the correlation we have between the variances of the 2 samples.

Step 1: Test hypothesis

 

Comparison of 2 averages

The test of comparing averages consists of the Shape following assumptions:

H : μ1 = μ2

H1 : μ1≠ μ2

Comparing 2 variances

The test of comparing variances consists of the following Shape assumptions:

H : σ1 = σ2

H1 : σ1≠ σ2

Step 2: Transforming data

Comparison of 2 averages

We Shape a new data that is:

DI = xi1 – xi2

Comparing 2 variances

The ratio of the 2 variances of the samples, but modified from the Pearson correlation coefficient1is used. To calculate it, two new variables are used:

UI = xi1 + xi2

VI = xi1 -xi2

Based on these data, the Pearson correlation coefficient is calculated which will later be tested.

 

 

Step 3: Practical value

Comparison of 2 averages

With:

  • dcross : average of the set DI of the data pairs
  • n: number of individual pairs in total
  • σD : standard deviation of differences DI

Comparing 2 variances

With:

  • r: Pearson correlation coefficient
  • n: number of individual pairs in total

Step 4: Calculating the critical value

The approximation of the student’s law is used to calculate the critical value (Excel function T.INV) for:

A probability that will depend on the meaning of the test is:

  • Bilateral: 1-α/2
  • Left unilateral: α
  • Right unilateral: 1-α

A number of degrees of freedom of N-1 (n being the number of data pairs).

Step 5: Calculate the p-Value

The p-Value depends on the size of the samples and the direction of the test. We use the p-Value calculated from the student’s law. We find:

  • For a bilateral test: Tdist (I practical value I; dof ; 2
  • For a unilateral left or right test: TDist (I practical value I; dof ; 1

With:

  • For a comparison of average: dof = n – 1
  • For a comparison of variance: dof = n – 2
  • n: number of data pairs

Step 6: Interpretation

Test directionResultStatistical conclusionPractical conclusion
BilateralPractical value > Critical value α/2
and
Practical value < Critical value 1 - α/2
We reject H0The 2 samples are different at the given level of risk α.
Unilateral rightPractical value < Critical value αWe reject H0Sample 1 is statistically larger than sample 2 at the given level of risk α.
Unilateral leftPractical value > Critical value αWe reject H0Sample 1 is statistically smaller than 2 at the given level of risk α.
ResultStatistical conclusionPractical conclusion
p-value > αWe retain H0Our 2 data series are identical or close with a risk of being wrong with p-value%
p-Value < αWe reject H0Our 2 series of data are statistically different with a risk of being wrong with p-value%

Source

1-R. Rafiq (2013) – population comparison, parametric tests

E. G. Pitman (1939) – A Note on normal correlation

A. Myers, C. H. Hansen (2003) – Experimental psychology

M. Vaubourdolle (2007) – Toxicology, mathematical, Physical and chemical sciences

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