Interpretation

Interpreting your results is about understanding what the statistical analysis of your data is telling you in the context of the problem you're trying to solve. It's about making sense of the numbers and drawing conclusions.

For example, let's say you're analyzing a dataset of student grades to understand how study time affects grades. You calculate the mean (average) grade for students who studied less than 2 hours per day and for those who studied more than 2 hours per day.

const lessThanTwoHours = students.filter(student => student.studyTime < 2)
const moreThanTwoHours = students.filter(student => student.studyTime > 2)

const meanLessThanTwoHours =
  lessThanTwoHours.reduce((sum, student) => sum + student.grade, 0) /
  lessThanTwoHours.length
const meanMoreThanTwoHours =
  moreThanTwoHours.reduce((sum, student) => sum + student.grade, 0) /
  moreThanTwoHours.length

Now, you need to interpret these results. If meanMoreThanTwoHours is significantly higher than meanLessThanTwoHours, you might conclude that studying more than 2 hours per day leads to better grades. However, you should also consider other factors that could affect grades (like attendance, prior knowledge, etc.) before drawing a definitive conclusion.

Remember, interpretation is not just about the statistical significance of the results, but also their practical significance. Even if the difference in means is statistically significant, if the difference is very small, it might not be practically significant. For example, if the mean grade for students who study more than 2 hours is 85 and for those who study less is 84.5, the difference might not be meaningful in a practical sense.