---
title: "Average growth vs treatment"
slug: "average-growth-vs-treatment"
updated: 2022-01-19T13:26:24Z
published: 2022-01-19T13:26:24Z
canonical: "userguide.dataclassroom.com/average-growth-vs-treatment"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://userguide.dataclassroom.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Average growth vs treatment

Let's say you are running an experiment to see how light affects plant growth. You will expose plants to either light or dark environments, and measure **their growth after a week**. You have three volunteers, Ali, Beth and Cy who will each manage a pair of plants. So six plants in all. [Back to overview...](/v1/docs/formatting-hints-and-tips)

Entering your results like this:

![](https://cdn.document360.io/62f8f41e-e041-42c4-b9bc-8ae1eb45d064/Images/Documentation/image-1637588313819.png)

This is a very traditional way to enter such results for graphing - it's simple, and can result in a bar chart of these average (mean) values like this:

![](https://cdn.document360.io/62f8f41e-e041-42c4-b9bc-8ae1eb45d064/Images/Documentation/image-1637588519555.png)

However, it has some disadvantages:

- There is no indication of how results were distributed by volunteer. Maybe this also has an effect, but you can't see it.
- By already calculating the averages, you can't see a view of how all the results were distributed.
- You can't do any form of statistical analysis on this data

DataClassroom can do a lot of the work for you, calculating averages and displaying graphs with many points.

We'd recommend that you record your data as one observation per row in a table, with columns that show the characteristics of each observation (your variables).

See here for an explanation.