Understanding Parametric and Non-Parametric Tests

What is the main focus of the video tutorial?

• Explaining the history of statistical tests

• Understanding parametric and non-parametric tests

• Learning about data visualization techniques

• Discussing the importance of data collection

Which distribution is typically assumed for parametric tests?

• Uniform distribution

• Exponential distribution

• Normal distribution

• Poisson distribution

What is the first step in hypothesis testing according to the video?

• Collecting data

• Checking assumptions

• Analyzing results

• Choosing a test

What should you do if your data is not normally distributed?

• Use a parametric test

• Use a non-parametric test

• Ignore the distribution

• Transform the data

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

• They work with any data type

• They are more powerful

• They require no assumptions

• They are easier to calculate

What is a key advantage of using parametric tests?

• They are less powerful

• They can detect smaller differences

• They have more assumptions

• They require larger sample sizes

What is the non-parametric counterpart of the t-test for independent samples?

• Mann-Whitney test

• Wilcoxon test

• Spearman correlation

• Kruskal-Wallis test

Which test is used for more than two dependent groups?

• Pearson correlation

• Kruskal-Wallis test

• ANOVA with repeated measures

• Simple t-test

Which test is used for one sample in non-parametric testing?

• Pearson correlation

• Wilcoxon test

• Paired t-test

• ANOVA

How can you perform statistical tests online using Data Tab?

• By using a mobile app

• By emailing your data to a statistician

• By visiting datadept.net and using the statistics calculator

• By downloading a software