 ## Inferential Statistics

Now you have collected your data you need to prove the results. Inferential statistics help you do this. Inferential statistics assess relationships or differences between data sets. For example if you wanted to find out whether PNF stretching increases hamstring flexibility, you would use an appropriate inferential test to get your answer.

Inferential tests can be subdivided into either Parametric or Non-Parametric

Parametric tests use

interval or ratio data and only use data that has normal distribution

There are 3 different Parametric tests you can choose from:

• Option 1 -Dependent T-Test

• Option 2 -Independent T-Test

• Option 3 -Pearson Product Moment Correlation Coefficient

Non-Parametric tests use

ordinal or nominal data

There are 3 different Non-Parametric tests you can choose from:

• Option 1 - Wilcoxon Matched-Pairs Signed-Ranks Test

• Option 2 - Mann-Whitney U Test

• Option 3 - Spearman's Rank Order Correlation Coefficient

## Option 1 -dependent T-Test

You use a dependent T-Test when you want to prove or disprove there is a difference between two related scores. By related scores it could mean comparing the same subjects' scores before and after an intervention. For example, is there a significant difference between subjects' number of shots on target before and after a practice drill?

After watching video: Your Excel is slightly different. Instead of going to "Tools" and then "Data Analysis", go to "Data" then "Data Analysis"

## Option 2-Independent T-Test

You use an independent T-Test when you want to prove or disprove there is a difference between two different group's scores. For example males compared to females, Man Utd compared to Fulham FC, Creatine compare to protein. For example, is there a significant difference between the group taking creatine and the group taking protein?

After watching video: Your Excel is slightly different. Instead of going to "Tools" and then "Data Analysis", go to "Data" then "Data Analysis"

## Option 3- Pearson Product-Moment Correlation Test

Used if you are trying to identify a relationship (correlation) between two variables. For example is there a correlation between number of calories eaten and amount of body fat? Is there a relationship between taking protein shakes and an increase in muscle mass? Is there a relationship/correlation between the number of shots taken by a player and their confidence levels?

After watching video: Your Excel is slightly different. Instead of going to "Tools" and then "Data Analysis", go to "Data" then "Data Analysis"

## Signed-Rank Test

The Wilcoxon matched-pairs signed-rank test is the non-parametric equivalent to the dependent t-test. Therefore, you would use if you are trying to see a difference between 2 scores from the same group and you are using nominal and ordinal data.

## Option 2 -Mann Whitney U Test

Mann Whitney U Test is the non-parametric equivalent to the independent t-test. Therefore, you would use if you are trying to see a difference between 2 different groups and you are using nominal and ordinal data.

## Order Correlation Test

Spearman's rank order correlation test is the non-parametric equivalent to the Pearson product-moment correlation coefficient test. Therefore, you would use if you are trying to see a relationship/ correlation between 2 variables and you are using nominal and ordinal data.