I part

1. Upload the data "gender_salary_data.csv" and name it "people".

2. Formulate your research question; which variables will you choose and what hypothesis will you
formulate? Formulate your null and alternative hypothesis.

3. Check the assumptions for the t-test.

3.1. Check the sample size.

3.2. Check the normality of the numerical variable. Use both graphical and analytical ways.

3.3. Using the tydiverse package, create two variables: "female" and "male" and save only females
and males from the people dataset.

3.4. Check normality of females and males.

3.5. Check another assumption - variance (spread) between the groups.

3.6. Test your hypothesis using an appropriate statistical test.

3.7. What conclusion can you make? Formulate the conclusion.

There is a statistically significant difference in salaries between males and females in Germany
(p-value=0.007), with females having a higher average salary than males.

When you’ve done I part, please, raise your hand.

II part

1. Upload the data "seafood_mercury_data.csv" and name it "mercury".

2. Formulate your research question; which variables will you choose and what hypothesis will you
formulate? Formulate your null and alternative hypothesis.

3. Check the assumptions for the t-test.

3.1. Check the sample size.

3.2. Check normality of the numerical variable. Use both graphical and analytical ways.

3.3. Using the tydiverse package, create two variables: "fish" and "seafood" and save only fish and
seafood from the mercury dataset.

3.4. Check the normality of fish and seafood.

3.5. The data doesn't meet the normality assumption. Use the nonparametric test to avoid data
transformation. Check the nonparametric test assumption - similar distributions between the groups.

3.6. Test your hypothesis using an appropriate statistical test.

3.7. What conclusion can you make? Formulate the conclusion.

There is no statistically significant difference in mercury levels between fish and seafood
(p-value=0.697).

Check list


                                                                       I part

                                                                                                                           II part

                            Null and alternative hypothesis (H0 and H1)



Assumptions for t-test/ Mann-Whitney U Test:

                                   Independence of observations

                                                                                                                       yes

                                                                                                                             yes

                                            Sample size



Normality of the whole numerical variable



Normality of each group



Variance between the groups



                                       Similar distributions

                                        between the groups



Test to perform:



Conclusion: