Apprendre Statistical Significance in Growth Experiments

Understanding whether a growth experiment's results are due to real effects or just random chance is crucial for making data-driven decisions. Statistical significance is the concept that helps you determine if the observed differences in your growth experiments, such as A/B tests, are meaningful or simply the result of variability in your data. By applying statistical tests, you can validate your experiment outcomes and avoid making changes based on misleading results.


              12345678910111213141516171819
            
import numpy as np
from scipy.stats import chi2_contingency

# Hardcoded A/B test results
# Group A: 200 users, 40 conversions
# Group B: 220 users, 60 conversions

# Build contingency table
# Rows: Groups (A, B)
# Columns: [Converted, Not Converted]
data = np.array([
    [40, 160],  # Group A
    [60, 160]   # Group B
])

chi2, p, dof, expected = chi2_contingency(data)

print("Chi-squared statistic:", chi2)
print("p-value:", p)

The code above uses scipy.stats.chi2_contingency to perform a chi-squared test on your A/B test data. The p-value tells you the probability of observing the difference between groups by random chance. A low p-value (commonly less than 0.05) suggests that the difference is statistically significant, meaning it's unlikely to have occurred by chance. If the p-value is high, you cannot confidently claim your experiment had a real effect. This approach helps you make decisions based on reliable evidence rather than luck or noise in your data.


              123456789101112131415161718
            
import numpy as np
from scipy.stats import chi2_contingency

# Adjusted experiment parameters
# Suppose you increase the number of users and conversions

# Group A: 400 users, 80 conversions
# Group B: 400 users, 120 conversions

data = np.array([
    [80, 320],   # Group A
    [120, 280]   # Group B
])

chi2, p, dof, expected = chi2_contingency(data)

print("Chi-squared statistic:", chi2)
print("p-value:", p)

Tout était clair ?

Merci pour vos commentaires !

Section 3. Chapitre 6

Demandez à l'IA

Posez n'importe quelle question ou essayez l'une des questions suggérées pour commencer notre discussion

Glissez pour afficher le menu


              12345678910111213141516171819
            
import numpy as np
from scipy.stats import chi2_contingency

# Hardcoded A/B test results
# Group A: 200 users, 40 conversions
# Group B: 220 users, 60 conversions

# Build contingency table
# Rows: Groups (A, B)
# Columns: [Converted, Not Converted]
data = np.array([
    [40, 160],  # Group A
    [60, 160]   # Group B
])

chi2, p, dof, expected = chi2_contingency(data)

print("Chi-squared statistic:", chi2)
print("p-value:", p)


              123456789101112131415161718
            
import numpy as np
from scipy.stats import chi2_contingency

# Adjusted experiment parameters
# Suppose you increase the number of users and conversions

# Group A: 400 users, 80 conversions
# Group B: 400 users, 120 conversions

data = np.array([
    [80, 320],   # Group A
    [120, 280]   # Group B
])

chi2, p, dof, expected = chi2_contingency(data)

print("Chi-squared statistic:", chi2)
print("p-value:", p)

Tout était clair ?

Merci pour vos commentaires !

Section 3. Chapitre 6

Statistical Significance in Growth Experiments

1. What does a low p-value indicate in an experiment?

2. Which scipy function is used for chi-squared tests?

3. Why is statistical significance important in growth hacking?

Statistical Significance in Growth Experiments

1. What does a low p-value indicate in an experiment?

2. Which scipy function is used for chi-squared tests?

3. Why is statistical significance important in growth hacking?