Parametric Tests 

Parametric Tests 

 

Parametric Tests

In parametric statistics, the population distribution is known and is based on a set of preset parameters.

When you use a parametric test, the distribution of values that you get from sampling is close to a normal distribution of values, or a bell curve. Common parametric tests look at and compare the mean or variance of data. Usually, parametric tests are thought to be more powerful than nonparametric tests (Knapp, 1998).

• T-test

In order to compare the difference in means between two groups, a test called the t-test can be used. The data from the two groups could be paired or unpaired. A paired t-test is used when we want to know the difference between two variables for the same person. Unpaired t-tests (or independent t tests) compare the difference in means between two groups of people to see if there is a significant difference between them. It is not possible to make comparisons between more than two groups using a t test (Xu et al., 2017).

In my research proposal, (In a rural ED setting, does placing an Advanced Practice Provider (APP) in triage during high volume times, compared to a nurse only triage approach, decrease LOS?) an unpaired or independent t test could be utilized. I am comparing the mean LOS between two independent groups with assumed equal variance (nurse only triage and APP in triage).  I will use data from patients during pre-intervention and post intervention and obtain a mean length of stay.

Assumptions made using an unpaired t test are the dependent variable (LOS) is normally distributed and the variance of data is the same between both nurse only triage and APP in triage (SPSS Tutorials: Independent Sample T Test, 2022).

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