Since the p-value is greater than 0.05, "no difference", right?
As an explanation of statistics, there are people who teach in such a way. As a writer, I don't agree.
The temptation to leave the judgment to the p-value and declare that "there is no statistical difference" when you can't tell by looking at the data is familiar to me, but it is not a good idea. If the data is something that cannot be understood by looking at the data, I think it is realistic to proceed by saying, "We will take measures in this way for the time being, but proceed with caution while watching the situation."
It's hard to put it in a straightforward way, but for example, the test for the difference between means examines whether the two numbers are statistically the same when there are two mean values. The degree to which they are statistically equal is calculated as the p-value.
For example, if there is a person who weighs 50.01 kg and a person who weighs 50.05 kg, do you conclude that there is a difference? That's the story. Weight changes over the course of the day, so if there is such a difference, I think it is normal to say that there is no difference. However, if you test for the difference in the mean value, even with such a difference, the p-value will be 0.0000001, and it may be said that there is a difference.
I explained from the example that even if the p-value is small, it does not conclude that there is a difference, but even if the p-value is large, as in your question, it may not be concluded that there is no difference.
In this case, you need to go back to the actual value of the data itself or the background of the individual data.
