Understanding Parametric and Non-Parametric Tests

What is a key assumption for using parametric tests?

• Data must be normally distributed

• Data must be binary

• Data must be ranked

• Data must be categorical

What is the primary focus of the video tutorial?

• Exploring statistical software

• Understanding hypothesis testing

• Explaining data collection methods

• Learning about data visualization

Why are parametric tests generally preferred over non-parametric tests?

• They require fewer assumptions

• They are more powerful

• They are easier to calculate

• They are more accurate

What is the main difference between Pearson and Spearman correlation?

• Pearson is for categorical data, Spearman is for continuous data

• Pearson uses raw data, Spearman uses ranks

• Pearson is non-parametric, Spearman is parametric

• Pearson uses ranks, Spearman uses raw data

In the context of hypothesis testing, what does the Mann-Whitney U test compare?

• Mean differences

• Rank sum differences

• Correlation differences

• Variance differences

What is a non-parametric counterpart to the T-test for independent samples?

• ANOVA

• Kruskal-Wallis test

• Mann-Whitney U test

• Chi-square test

What does the T-test for independent samples assess?

• Rank sum differences

• Mean differences

• Variance differences

• Correlation differences

Which test is used when data is not normally distributed?

• Pearson correlation

• T-test

• ANOVA

• Mann-Whitney U test

When should you consider using a non-parametric test?

• When data is normally distributed

• When data is categorical

• When data is binary

• When data does not meet parametric test assumptions

What tool is mentioned for performing hypothesis tests online?

• Data Tab

• R Studio

• SPSS

• Excel