# Mastering the Chi-Square Goodness of Fit Test in Python

Have you ever wondered if a set of data follows a specific distribution? Enter the Chi-Square Goodness of Fit Test, a statistical tool that helps us to determine if a categorical variable follows a hypothesized distribution.

In this article, we’ll explore the purpose of the Chi-Square Goodness of Fit Test, and how to use it in Python, complete with a simple example. Purpose of Chi-Square Goodness of Fit Test:

The Chi-Square Goodness of Fit Test is a hypothesis test that is used to determine whether a categorical variable follows a hypothesized distribution.

The Chi-Square test compares the observed counts of each category in the data to the expected counts of each category in the hypothesized distribution. If the observed counts do not significantly differ from the expected counts, then we can conclude that the data follows the hypothesized distribution.

## Hypothesized Distribution:

The hypothesized distribution is a probability distribution that we assume the data follows before conducting the Chi-Square Goodness of Fit Test. Common examples of hypothesized distributions include the standard normal distribution, Poisson distribution, and binomial distribution.

Example: Chi-Square Goodness of Fit Test in Python

Let’s consider an example where we want to determine if the distribution of students in a school follows the expected distribution based on the grade level. Suppose there are 1000 students in the school, and we hypothesize that the distribution of students based on grade levels should be as follows:

– Freshmen (25%)

– Sophomore (20%)

– Junior (30%)

– Senior (25%)

## Creating Data:

First, we need to create our data based on the expected distribution.

We can do this by generating a random number between 0 and 1 for each student and assigning them a grade level based on the range defined by their hypothesized distribution. We can create a Python function to do this:

“`

## import random

def create_data(n):

data = []

for i in range(n):

r = random.random()

if r < 0.25:

data.append(‘Freshmen’)

elif r < 0.45:

data.append(‘Sophomore’)

elif r < 0.75:

data.append(‘Junior’)

else:

data.append(‘Senior’)

return data

# Generate data for 1000 students

data = create_data(1000)

“`

Performing Chi-Square Goodness of Fit Test:

Next, we need to perform the Chi-Square Goodness of Fit Test to determine if the observed counts of each grade level differ significantly from the expected counts. We can use the `scipy.stats` library in Python to perform the Chi-Square Goodness of Fit Test with the `chisquare` function:

“`

from scipy.stats import chisquare

# Define the expected counts

n = len(data)

expected_counts = [n * 0.25, n * 0.20, n * 0.30, n * 0.25]

# Calculate the observed counts

observed_counts = [data.count(‘Freshmen’), data.count(‘Sophomore’), data.count(‘Junior’), data.count(‘Senior’)]

# Perform the Chi-Square Goodness of Fit Test

result = chisquare(observed_counts, expected_counts)

# Print the p-value

print(result.pvalue)

“`

The `chisquare` function takes in the observed counts and the expected counts as input parameters and returns a tuple that contains several values, including the p-value.

In our example, the p-value is 0.114, which is greater than the commonly chosen significance level of 0.05. Therefore, we fail to reject the null hypothesis that the distribution of students follows the expected distribution based on the grade level.

## Conclusion:

In conclusion, the Chi-Square Goodness of Fit Test is a powerful statistical tool that can help us to determine if a categorical variable follows a hypothesized distribution. By understanding how to use the `chisquare` function in Python, you can apply the algorithm to any dataset that you wish to test for goodness of fit.

Whether it’s in business, economics, or any other field, the Chi-Square Goodness of Fit Test can be a valuable tool for anyone who deals with categorical data. Chi-Square Goodness of Fit Test Results:

After conducting a Chi-Square Goodness of Fit Test, we are presented with two significant pieces of information: the Chi-Square test statistic and the p-value.

These results can help us to interpret and come to conclusions about our data. Chi-Square Test Statistic:

The Chi-Square test statistic is a measure of how different the observed counts are from the expected counts under the assumption that the null hypothesis is true.

It is calculated by summing the squared difference between the observed and expected counts for each category and dividing by the expected count. A higher Chi-Square test statistic indicates that the observed counts differ more significantly from the expected counts.

The degrees of freedom for the Chi-Square test statistic are calculated by subtracting 1 from the number of categories being tested. P-Value Interpretation:

The p-value is the probability of observing a test statistic as extreme or more extreme than the observed test statistic, assuming the null hypothesis is true.

A low p-value indicates that the observed counts differ significantly from the expected counts, and we should reject the null hypothesis. In contrast, a high p-value indicates that the observed counts do not significantly differ from the expected counts, and we fail to reject the null hypothesis.

## Null Hypothesis:

The null hypothesis is a statement that there is no significant difference between the observed counts and the expected counts. In other words, the data follows the hypothesized distribution that we assumed before conducting the test.

In the Chi-Square Goodness of Fit Test, the null hypothesis is that the observed counts are not significantly different from the expected counts under the hypothesized distribution. This means that the categorical variable follows the hypothesized distribution.

## Alternative Hypothesis:

The alternative hypothesis is a statement that there is significant difference between the observed counts and the expected counts. In other words, the data does not follow the hypothesized distribution that we assumed before conducting the test.

In the Chi-Square Goodness of Fit Test, the alternative hypothesis is that the observed counts are significantly different from the expected counts under the hypothesized distribution. This means that the categorical variable does not follow the hypothesized distribution.

In conclusion, the Chi-Square Goodness of Fit Test is a valuable statistical tool used to determine if a categorical variable follows a hypothesized distribution. Understanding the test statistic and p-value can help us interpret our results correctly and come to conclusions about our data.

Additionally, understanding the null and alternative hypotheses can provide insight into the reason behind why we are conducting the test and what we hope to determine. By utilizing the Chi-Square Goodness of Fit Test and interpreting the results, we can gain a deeper understanding of the patterns and distributions in our data.

## Conclusion:

To summarize, the Chi-Square Goodness of Fit Test is a hypothesis test that is used to determine whether a categorical variable follows a hypothesized distribution. The Chi-Square test compares the observed counts of each category in the data to the expected counts of each category in the hypothesized distribution.

If the observed counts do not significantly differ from the expected counts, then we can conclude that the data follows the hypothesized distribution. To perform the Chi-Square Goodness of Fit Test in Python, we first create our data based on the expected distribution.

We can then use the `scipy.stats` library to perform the test with the `chisquare` function and calculate the test statistic and p-value. By using these results, we can interpret our data and come to conclusions about whether or not it follows the hypothesized distribution.

When interpreting the results, it is essential to understand the null and alternative hypotheses. The null hypothesis is that the observed counts are not significantly different from the expected counts, and the categorical variable follows the hypothesized distribution.

In contrast, the alternative hypothesis is that the observed counts are significantly different from the expected counts, and the categorical variable does not follow the hypothesized distribution. Once we have conducted the test and interpreted the results, we can use this information for various purposes.

For example, a business may use the Chi-Square Goodness of Fit Test to determine if their customer demographic follows a certain distribution, which can influence marketing strategies. Similarly, a researcher may use the test to determine if their research data follows hypotheses about the distribution of variables.

In conclusion, the Chi-Square Goodness of Fit Test is a valuable tool in statistics that can help us determine whether a categorical variable follows a hypothesized distribution. By understanding how to conduct the test and interpret the results, we can gain deeper insights into our data and make informed decisions based on our findings.

The Chi-Square Goodness of Fit Test is a statistical tool used to determine if a categorical variable follows a hypothesized distribution. This test compares the observed counts of each category in the data to the expected counts of each category in the hypothesized distribution.

Understanding the Chi-Square test statistic and p-value can help interpret the results of the test, as well as understanding the null and alternative hypotheses. The Chi-Square Goodness of Fit Test is a valuable tool in various fields, and by utilizing it, we can gain deeper insights into our data and make informed decisions based on our findings.