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What is the G-Test?
The G-Test is a statistical test used to determine if the proportions of categories in two group variables significantly differ from each other. To use this test, you should have two group variables with two or more options and you should have more than 1000 values in total. See more below.
The G-Test is also called the Likelihood Ratio Test, or the Log-Likelihood Ratio Test.
Assumptions for the G-Test
Every statistical method has assumptions. Assumptions mean that your data must satisfy certain properties in order for statistical method results to be accurate.
The assumptions for the G-Test include:
- Random Sample
- Mutually exclusive groups
Let’s dive into what that means.
The data points for each group in your analysis must have come from a simple random sample. This is important because if your groups were not randomly determined then your analysis will be incorrect. In statistical terms this is called bias, or a tendency to have incorrect results because of bad data.
Each of your observations (data points) should be independent. This means that each value of your variables doesn’t “depend” on any of the others. For example, this assumption is usually violated when there are multiple data points over time from the same unit of observation (e.g. subject/customer/store), because the data points from the same unit of observation are likely to be related or affect one another.
Mutually Exclusive Groups
The two groups of your categorical variable should be mutually exclusive. For example, if your categorical variable is hungry (yes/no), then your groups are mutually exclusive, because one person cannot belong to both groups at once.
When to use the G-Test?
You should use the G-Test in the following scenario:
- You want to test the difference between two variables
- Your variable of interest is proportional or categorical
- You have two or more options
- You have independent samples
- You have more than 1000 values in total
Let’s clarify these to help you know when to use the G-Test.
You are looking for a statistical test to look at how a variable differs between two groups. Other types of analyses include testing for a relationship between two variables or predicting one variable using another variable (prediction).
Proportional or Categorical
For this test, your variable of interest must be proportional or categorical. A categorical variable is a variable that contains categories without a natural order. Examples of categorical variables are eye color, city of residence, type of dog, etc. Proportional variables are derived from categorical variables, for instance: the number of people that converted on two different versions of your website (10% vs 15%), percentages, the number of people who voted vs people who did not vote, the proportion of plants that died vs survived an experimental treatment, etc.
If you want to compare two continuous variables, you may want to use an Independent Samples T-Test.
Two or More Options
Your categorical variables should have two or more possible options. Some examples of variables like this are made a purchase (yes/no), color (if just black/white), recovered from disease (yes/no).
Independent samples means that your two groups are not related in any way. For example, repeated measurements from the same group over time are often not independent samples, because each observation from the same person is likely related to other samples from that person.
If you have repeated measures from a single sample, you should consider using the McNemar Test.
More than 1000 values total
The rule-of-thumb we recommend is to use this test when you have 1000 or more observations total.
If you have fewer than 10 in a cell, we recommend using Fisher’s Exact Test. If you have more than 10 in every cell, fewer than 1000 total observations, and only two options in your categorical variable, we recommend using the Two-Proportion Z-Test. If you have more than 10 in every cell, fewer than 1000 total observations, and more than two options in your categorical variable, we recommend using the Chi-Square Test of Independence.
Group: Treatment (A/B)
Variable: Recovered from disease (yes/no)
In this example, we are interested in investigating whether our two treatment groups differ significantly in rate of recovery from disease. The null hypothesis is that there is no difference between recovery rates between the two groups.
Because our variable is binary with only two possible values (yes/no), and our data meet all other assumptions, we know that the G-Test is appropriate to use. The G-test would also be appropriate if there were more than two options in our categorical variable.
The analysis will result in a probability or p-value. The p-value represents the chance of seeing our results if there was actually no difference in recovery rate between the two treatment types. A p-value less than or equal to 0.05 means that our result is statistically significant and we can trust that the difference is not due to chance alone.
Frequently Asked Questions
Q: How do I run the G-Test in R?
A: StatsTest is focused on helping you pick the right statistical method every time. There are many resources available to help you figure out how to run this method with your data:
R article: https://rcompanion.org/rcompanion/b_06.html
If you still can’t figure something out, feel free to reach out.