ANOVA in Data Analysis Python

Introduction

ANOVA helps us find out if different groups have different average values. It tells us if the differences we see are real or just by chance.

Comparing average test scores of students from three different schools.

Checking if three types of fertilizers affect plant growth differently.

Seeing if different diets lead to different average weights in a group of people.

Testing if three brands of batteries last different amounts of time.

Syntax

Data Analysis Python

from scipy.stats import f_oneway

f_oneway(group1, group2, group3, ...)

Each group is a list or array of numbers representing one category.

The function returns an F-statistic and a p-value to help decide if groups differ.

Examples

Compare two groups to see if their averages differ.

Data Analysis Python

from scipy.stats import f_oneway

f_oneway([5, 6, 7], [8, 9, 10])

Compare three groups to check for differences in their means.

Data Analysis Python

from scipy.stats import f_oneway

f_oneway([20, 22, 19], [30, 29, 31], [25, 27, 26])

Sample Program

This code compares the average test scores of three classes to see if they differ significantly.

Data Analysis Python

from scipy.stats import f_oneway

# Data: test scores from three classes
class1 = [85, 88, 90, 92, 87]
class2 = [78, 75, 80, 79, 77]
class3 = [90, 92, 94, 91, 89]

# Perform ANOVA test
f_stat, p_value = f_oneway(class1, class2, class3)

print(f"F-statistic: {f_stat:.2f}")
print(f"p-value: {p_value:.4f}")

Important Notes

A small p-value (usually less than 0.05) means the groups have different averages.

ANOVA assumes the data in each group is roughly normal and has similar spread.

If ANOVA shows differences, you can do more tests to find which groups differ.

Summary

ANOVA tests if multiple groups have different average values.

It uses F-statistic and p-value to decide if differences are real.

Useful when comparing three or more groups at once.