Aprende 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.


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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.


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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)

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 3. Capítulo 6

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Suggested prompts:

What does a lower p-value in the second experiment indicate?

Can you explain how increasing the sample size affects statistical significance?

How do I interpret the chi-squared statistic in these results?

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              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)

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 3. Capítulo 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?