---
title: "Interpretation"
description: "Guidelines for interpreting statistical analysis results."
date: "2025-10-02"
tags: ["data-analysis","interpretation","guide"]
canonical: "/learn/tutorials/data-analysis/06-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.

```ts
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.