Certainly, I can guide you through the process of conducting a One Sample T Test in SPSS and provide a comprehensive analysis and interpretation of the results. Here are the steps to follow: **Step 1: Launch SPSS and Access the Analyze Panel** - Open your SPSS software. - In the menu bar, go to "Analyze." **Step 2: Navigate to "Compare Means" and Select "One Sample T Test"** - Under the "Analyze" menu, select "Compare Means." - In the submenu that appears, select "One Sample T Test." **Step 3: Enter the Test Value** - In the "One Sample T Test" dialog box that appears, you will see a field labeled "Test Value." - Here, you should enter the mean score of the variable for which you want to conduct the One Sample T Test. Make sure you enter the correct value. **Step 4: Perform the One Sample T Test** - Once you have entered the test value, click the "OK" button to perform the One Sample T Test. After performing the One Sample T Test, you will obtain several important statistical values. These include: 1. **Sum of Squares:** This value represents the sum of the squared differences between each data point and the sample mean. 2. **Degrees of Freedom (df):** Degrees of freedom represent the number of values in the final calculation of a statistic that are free to vary. In the case of a One Sample T Test, df is equal to the sample size minus 1 (df = n - 1), where 'n' is the number of data points in your sample. 3. **Mean Difference:** This is the difference between the sample mean and the test value you entered. It helps you understand the direction of the difference. 4. **Sig (2-Tailed):** This is the p-value associated with the test. It tells you the probability of observing the sample data if the population mean were equal to the test value. A small p-value (typically less than 0.05) indicates statistical significance. 5. **t Statistics:** The t-statistic is a measure of how many standard errors your sample mean is away from the test value. A larger absolute t-value suggests a more significant difference. **Data Analysis and Interpretation:** To determine whether there is a significant difference between the sample mean and the test value, you should focus on the "Sig (2-Tailed)" value. Here's how to interpret it: - If Sig (2-Tailed) < 0.05 (or your chosen significance level), you can conclude that there is a significant difference between the sample mean and the test value. In other words, the data provide evidence to reject the null hypothesis. - If Sig (2-Tailed) ≥ 0.05, you cannot conclude that there is a significant difference. In this case, the data do not provide enough evidence to reject the null hypothesis, suggesting that the sample mean is not significantly different from the test value. Remember that the significance level (usually set at 0.05) determines the threshold for statistical significance. You should also consider the direction of the difference by examining the "Mean Difference" value. Please ensure that you have accurately followed the steps and entered the correct values to avoid errors in your statistical analysis. Certainly, let's analyze the results of your One Sample T Test: 1. **Test Value:** The test value you entered is 1.76667, which serves as a reference point for your analysis. 2. **t:** The t-statistic for your test is approximately 0.000. This statistic measures how many standard errors your sample mean is away from the test value. In this case, it's very close to zero, indicating that the sample mean is very similar to the test value. 3. **Degrees of Freedom (df):** The degrees of freedom for your test is 599. This is calculated as the sample size minus one (df = n - 1), where 'n' is the number of data points in your sample. 4. **Sig. (2-tailed):** The p-value associated with your test is 1.000. This is a very high p-value, meaning there is no statistical significance. In fact, it's equal to 1, which is the highest possible value. This suggests that there is no significant difference between the sample mean and the test value of 1.76667. 5. **Mean Difference:** The mean difference is approximately -0.000003. This value represents the difference between your sample mean and the test value. In this case, it's very close to zero, indicating that the sample mean is almost identical to the test value. 6. **95% Confidence Interval of the Difference:** The 95% confidence interval provides a range within which the true population mean is likely to fall. In your case, the confidence interval ranges from approximately -0.07173 (Lower) to 0.07172 (Upper). This interval includes zero, further indicating that there is no significant difference between the sample mean and the test value. **Data Analysis and Interpretation:** Based on the results: - The very high p-value (Sig. 2-tailed = 1.000) suggests that there is no statistical significance. This means that the data do not provide enough evidence to reject the null hypothesis, indicating that the sample mean age is not significantly different from the test value of 1.76667. - The mean difference is extremely close to zero (-0.000003), which reinforces the conclusion that there is almost no difference between the sample mean and the test value. - The 95% confidence interval includes zero, which also supports the finding that there is no significant difference. In summary, the One Sample T Test results suggest that the sample mean age is not significantly different from the test value of 1.76667. There is no statistical evidence to suggest otherwise.