Agriculture is one of the oldest practices known to man, and throughout history, farmers have sought to maximize their crop yield. One way to achieve this is through the use of fertilizers. However, with so many types of fertilizers available, it can be challenging for farmers to determine which one is the most effective for their crops.

In this use case, we explore the power of ANOVA testing in analyzing the effect of different types of fertilizers on crop yield. By conducting a randomized experiment and measuring the crop yield for each fertilizer group, we can determine if there is a statistically significant difference in mean crop yield between the groups. This information can then be used by farmers to make informed decisions on which fertilizer to use in future growing seasons to maximize their crop yield.

Here is a hypothetical dataset of crop yields for each fertilizer group:

Fertilizer A: 500, 550, 600, 575, 525
Fertilizer B: 700, 650, 750, 725, 675
Fertilizer C: 400, 450, 500, 475, 425

By conducting an ANOVA test on this dataset, we can determine if there is a statistically significant difference in the mean crop yield between the three groups.
If the p-value is less than the chosen alpha level (e.g. 0.05),
we can reject the null hypothesis that there is no difference in the mean crop yield between the groups, and conclude that there is a significant difference in crop yield among the fertilizers which is our alternate hypothesis.

This information can then be used by the farmer to make informed decisions on which fertilizer to use in future growing seasons in order to maximize crop yield.

From the above screenshot we can see the ANOVA test has concluded that there is a difference in the performance of the three types of fertilizers. Thus we reject our null hypothesis and go with our alternative hypothesis. We can specifically conclude that Fertilizer B provides more yield so the farmer will choose to use Fertilizer B for his crops for the maximum yield.