Why statistics validates hypotheses in Data Analysis Python - See It in Action

Why Statistics Validates Hypotheses

📖 Scenario: Imagine you want to know if a new teaching method helps students score better on tests. You collect test scores from two groups: one with the old method and one with the new method. You want to use statistics to check if the new method really makes a difference or if the scores are just by chance.

🎯 Goal: Build a simple Python program that uses statistics to check if the difference between two groups' test scores is meaningful. You will create data, set a threshold for significance, calculate the average scores, and compare them to decide if the new method is better.

📋 What You'll Learn

Create two lists called old_method_scores and new_method_scores with exact test scores.

Create a variable called significance_level set to 0.05.

Calculate the average score for each group using a for loop and store them in avg_old and avg_new.

Compare the averages and print a message if the new method is better by more than the significance level.

💡 Why This Matters

🌍 Real World

Scientists and researchers use statistics to check if their ideas or new methods really work or if results happened by chance.

💼 Career

Data analysts and scientists often validate hypotheses to make decisions based on data, such as improving products, treatments, or processes.

1

Create test score lists

Create two lists called old_method_scores and new_method_scores with these exact values: old_method_scores = [70, 75, 80, 65, 90] and new_method_scores = [78, 82, 85, 80, 88].

Data Analysis Python

# Create the two lists with test scores
# Your code here

Hint

Use square brackets [] to create lists and separate numbers with commas.

2

Set significance level

Create a variable called significance_level and set it to 0.05.

Data Analysis Python

old_method_scores = [70, 75, 80, 65, 90]
new_method_scores = [78, 82, 85, 80, 88]

# Set the significance level to 0.05
# Your code here

Hint

Use a simple assignment with = to set the variable.

3

Calculate average scores

Use a for loop with variable score to calculate the average of old_method_scores and store it in avg_old. Then use another for loop with variable score to calculate the average of new_method_scores and store it in avg_new.

Data Analysis Python

old_method_scores = [70, 75, 80, 65, 90]
new_method_scores = [78, 82, 85, 80, 88]
significance_level = 0.05

# Calculate average of old_method_scores
# Your code here

# Calculate average of new_method_scores
# Your code here

Hint

Start with a sum variable at 0, add each score in the loop, then divide by the number of scores using len().

4

Compare averages and print result

Write an if statement to check if avg_new is greater than avg_old by more than significance_level. If true, print "New method is better by a meaningful margin!". Otherwise, print "No meaningful improvement detected.".

Data Analysis Python

old_method_scores = [70, 75, 80, 65, 90]
new_method_scores = [78, 82, 85, 80, 88]
significance_level = 0.05

sum_old = 0
for score in old_method_scores:
    sum_old += score
avg_old = sum_old / len(old_method_scores)

sum_new = 0
for score in new_method_scores:
    sum_new += score
avg_new = sum_new / len(new_method_scores)

# Compare averages and print the result
# Your code here

Hint

Use if avg_new - avg_old > significance_level: to check the difference and print() to show the message.