One-Sample T Test - Pertemuan 6

Summary

In this video, Puspa Nurafifah explains how to perform a one-sample t-test using SPSS. She walks viewers through the purpose of the test, which compares the sample mean to a known population mean, and discusses the assumptions of normal distribution for the data. She demonstrates how to check for normality, interpret the results using significance levels, and explains different methods for decision-making, including comparing t-values with t-tables or using SPSS outputs. The video concludes by rejecting or accepting hypotheses based on the results, helping viewers understand statistical testing in real-world scenarios.

Takeaways

• 😀 The video introduces the concept of the one-sample t-test using SPSS for statistical analysis.
• 😀 A one-sample t-test compares the mean of a sample to a known value or population mean, and it's commonly used in hypothesis testing.
• 😀 Three types of t-tests are discussed: one-sample, independent sample, and paired sample t-tests.
• 😀 The one-sample t-test checks if the sample mean is different from a hypothesized population mean.
• 😀 The normality of data must be checked before performing a t-test; data should follow a normal distribution.
• 😀 If the significance value (p-value) is greater than 0.05, the data is considered normally distributed.
• 😀 In the example, the hypothesis is that the average UTS score of active students in organizations is 74.
• 😀 SPSS is used to test normality and perform the t-test. If the p-value is less than 0.05, the null hypothesis is rejected.
• 😀 The video demonstrates how to interpret SPSS output, comparing the calculated t-value to the critical t-value from the t-distribution table.
• 😀 The conclusion from the example is that the average UTS score of active students is not equal to 74, as the null hypothesis is rejected.

Q & A

What is the purpose of conducting a one-sample t-test in the script?

The purpose of the one-sample t-test is to compare the average of a sample to a known population mean, to determine if there is a significant difference between them.

What is the assumption that needs to be fulfilled for the one-sample t-test?

The assumption is that the data must follow a normal distribution. This is a key requirement for performing a one-sample t-test.

How can you check if the data is normally distributed before conducting a one-sample t-test?

You can check the normality of the data using SPSS by conducting a test of normality, such as the Shapiro-Wilk test. If the significance value is greater than 0.06, the data is considered normally distributed.

In the script, what is the null hypothesis (H0) for the one-sample t-test?

The null hypothesis (H0) is that the average value of the students' midterm scores (UTS) for active students in organizations is equal to 74.

What is the decision rule based on significance for the one-sample t-test?

If the significance value is less than 0.05, then the null hypothesis (H0) is rejected. If the significance value is greater than 0.05, the null hypothesis is accepted.

What does a significance value of 0.03 imply in the context of the test?

A significance value of 0.03, which is less than 0.05, implies that the null hypothesis (H0) is rejected. Therefore, the average UTS score for active students in organizations is not equal to 74.

How do you perform a one-sample t-test in SPSS based on the script?

To perform a one-sample t-test in SPSS, you need to input the data, select 'Analyze' > 'Compare Means' > 'One-Sample T-Test,' then input the hypothesized population mean (e.g., 74), and click 'OK' to run the test.

What is the importance of the t-table in hypothesis testing, as mentioned in the script?

The t-table is used to compare the t-value calculated in SPSS with a critical t-value from the table. If the calculated t-value is greater than the critical t-value, the null hypothesis is rejected.

What does it mean if the calculated t-value is greater than the critical t-value from the t-table?

If the calculated t-value is greater than the critical t-value from the t-table, it means that the null hypothesis (H0) is rejected, indicating a significant difference between the sample mean and the hypothesized population mean.

What does the graphical representation (curve) tell us in hypothesis testing according to the script?

The graphical representation (curve) shows the rejection areas for the null hypothesis. If the t-value falls into the rejection area (on either side of the curve), the null hypothesis is rejected.