BAB V STATISTIKA NON PARAMETRIK|INDEPENDENT SAMPLE|STATISTIKA TERAPAN|Part II

Summary

This video tutorial provides a step-by-step guide on performing hypothesis testing for two independent samples using SPSS, focusing on Chi-Square tests. It covers two distinct cases: one testing if more female students participate in the Merdeka Internship Program compared to male students, and another analyzing if age influences preferred sports (soccer vs. golf). The video walks through the necessary setup in SPSS, including creating variables, inputting data, and conducting Chi-Square tests, with clear instructions on interpreting the results to draw meaningful conclusions.

Takeaways

• 😀 Chi-Square tests are used for hypothesis testing on two independent samples in SPSS.
• 😀 The first exercise involves testing whether female students participate more in the Merdeka Internship Program than male students.
• 😀 Null Hypothesis (H₀) for the first test states that female and male student participation rates are equal.
• 😀 The alternative hypothesis (H₁) for the first test claims that more female students participate than male students.
• 😀 The second exercise involves testing the relationship between age groups and sports preferences (football vs. golf).
• 😀 Null Hypothesis (H₀) for the second test assumes no relationship between age group and sports preference.
• 😀 SPSS is used to define variables, input data, and perform Chi-Square tests on both exercises.
• 😀 In the first exercise, the p-value of 0.006 indicates that the null hypothesis is rejected, confirming a significant difference in participation between female and male students.
• 😀 In the second exercise, the p-value of 0.104 indicates that the null hypothesis is accepted, suggesting no significant relationship between age group and sports preference.
• 😀 The Chi-Square test uses categorical data, such as participation status and sport preferences, to test hypotheses regarding proportions or relationships.
• 😀 To perform the analysis in SPSS, variables are defined and weighted appropriately before running the Chi-Square tests.

Q & A

What is the main objective of the video?

The main objective of the video is to demonstrate how to perform hypothesis testing for two independent samples using SPSS, focusing on Chi-Square tests.

What is the hypothesis testing example in the first part of the video about?

The first example is about testing whether more female students are participating in the Merdeka Internship Program compared to male students, using the Chi-Square test for independence.

What are the null and alternative hypotheses in the first example?

The null hypothesis (H0) states that the proportion of female students participating in the program is equal to that of male students, while the alternative hypothesis (H1) states that the proportion of female students is greater than that of male students.

What significance level is used in both examples in the video?

A significance level of 0.05 (5%) is used in both examples to test the hypotheses.

What is the process to input data into SPSS for the first example?

In SPSS, create variables for 'Status' (Female, Male), 'Program Participation' (Yes, No), and 'Frequency' (number of students). Enter the data for both male and female students based on the provided problem statement.

How do you interpret the results of the Chi-Square test in the first example?

The p-value from the Chi-Square test is compared to the significance level of 0.05. If the p-value is less than 0.05, the null hypothesis is rejected. In this case, the p-value is 0.00006, which is less than 0.05, so the null hypothesis is rejected, confirming that more females participate in the internship program.

What does the Chi-Square test for independence examine in the second example?

The Chi-Square test for independence in the second example examines whether there is a relationship between age groups (Children, Teenagers, Adults) and sport preferences (Football, Golf).

What were the results of the second Chi-Square test in the video?

The p-value from the second Chi-Square test is 0.104, which is greater than 0.05. Therefore, the null hypothesis is not rejected, meaning there is no significant relationship between age group and sport preference.

What software is used in the video to perform the hypothesis tests?

The video demonstrates how to use SPSS software to perform the hypothesis tests for both examples.

What are the two Chi-Square tests demonstrated in the video?

The first test is the Chi-Square test for corrected continuity, and the second test is the Chi-Square test for independence.