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Analyzing Rice Production Changes with a Paired t-Test Before and After Training Using Excel

Evaluating the effectiveness of extension programs is crucial to ensure that the interventions implemented provide positive impacts for farmers. One way to measure this effectiveness is by comparing production before and after the program using a paired t-test.

This article is written by Kanda Data to explain how to perform a paired t-test in Excel using a case study of rice production. We have data from 30 farmers showing their rice production (in tons) before and after the extension program. Here is the data we will use:

Steps to Perform a Paired t-Test in Excel: Begin by entering the data into two columns in Excel, with “Production Before” in one column and “Production After” in the other.

To enable the Data Analysis Toolpak: Open File, select Options, then choose Add-Ins. Click Go and check the Analysis ToolPak box, then click OK.

Steps for the paired sample t-test: Go to the Data tab and click Data Analysis. Next, select t-Test: Paired Two Sample for Means and click OK. Enter the ranges for: “Variable 1 Range” (Rice production before the training program) and “Variable 2 Range” (Rice production after the training program).

Make sure to check the Labels box if you included column headers. Set the Alpha value to 0.05 and choose the output range. Detailed analysis steps can be seen in the image below:

Excel will generate the results of the t-test, as shown in the image below:

Based on the image above, look at the t-Statistic and p-value in the output. We see the p-value for the two-tail test is 2.267E-24, which is less than 0.05. Therefore, we reject the null hypothesis, indicating that there is a significant difference between production before and after the extension program.

Through this paired t-test analysis, we can evaluate whether the extension program had a significant impact on rice production for the farmers. This method provides a robust way to analyze data and make evidence-based decisions.

Thank you for reading! If you found this article helpful, please share it and subscribe to our blog to get more tips and data analysis tutorials from Kanda Data.

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