Parametric and Non-Parametric Tests Flashcards
What are the Parametric and Non-Parametric tests and what makes them different?
2 categories of statistical testing.
Parametric relies on their being parameters that must be met
Non-parametric does not rely on any prior parameters.
What are the criteria that are needed to do a parametric test?
- Ratio-level data
- Typically assumes normal distribution
- Typically assumes similar variance
Non-parametric tests are used on all data types and make no assumptions about the distribution or variance.
List the stages of the parametric check list
- Is your data ratio-level? if not then use NP
- Assess normality - normal distribution? if not use NP
- Assess similarity of variance - if SD of MOST variable sampleis < 10 x SD of least variable sample the variance is sufficiently similar to proceed with parametric.
When would you not use the parametric check list?
with small samples. Select parametric if you see no reason to think it won’t fit the criteria. Otherwise the non-parametric is never wrong.
Can you do a parametric test if any one of the check list is not comliant with the criteria?
No! You would do a non-parametric test.
Aside from the frequency histograms, what would you use to test for normality?
- Skewness and Kurtosis
- Both are measures of how distored the curve is from normal distribution. - Kolmogorov-smirnov test (for N >2000 - not often used)
- Shapiro-Wilk test (N <2000)
How do you work out the skewness and Kurtosis result from the statistics provided?
on SPSS the tests produce a Statistic and Standard Error numbers.
Statistic/Std error = skewness/kurtosis
The result must be within -2 to +2 for the distribution to be normal, thus thumbs up for parametric! Outside these parameters and you must use NP.
What results do you get in a shapiro-wilk output and which one is of most interest?
- S-W statistic
- degree of freedom (df)
- Sig value (P-value)
Sig value is of most interest.
What does the Ho for the Shapiro-Wilk test predict?
That there IS normal distribution.
<0.05 we ACCEPT the Ho that our data is NORMALLY distributed - Parametric
>0.05 we REJECT the Ho that our data is skewed - NP
What is used to test Equal Variance (or homogeneity of variance)?
Levenes test as calculated on the mean (there are a number of different results which are based on other descriptive stats e.g. median)
We look at the Sig value:
<0.05 we REJECT the Ho because they DO have equal variance - Parametric
>0.05 we ACCEPT the Ho that there is NO equal variance - NP