Stop using frequentist approaches for A/B Testing.
Use Bayesian instead.
Bayesian has 5 key advantages: 🧵
#DataScience #Bayesian #Rstats #Python
Use Bayesian instead.
Bayesian has 5 key advantages: 🧵
#DataScience #Bayesian #Rstats #Python
1. Intuitive Interpretation:
Bayesian methods provide results in terms of probabilities.
Bayesian probabilities are more intuitive to understand AND more accurate compared to t-test or linear regression p-values.
Bayesian methods provide results in terms of probabilities.
Bayesian probabilities are more intuitive to understand AND more accurate compared to t-test or linear regression p-values.
2. Incorporation of Prior Knowledge:
Bayesian analysis allows the incorporation of prior beliefs or existing data into the analysis.
This is particularly useful when historical data is available.
Bayesian analysis allows the incorporation of prior beliefs or existing data into the analysis.
This is particularly useful when historical data is available.
3. Flexibility in Sample Size:
Unlike frequentist approaches that require a fixed sample size determined in advance, Bayesian methods can adapt to varying sample sizes.
This is a huge benefit for companies that want results faster.
Bayesian can help.
Unlike frequentist approaches that require a fixed sample size determined in advance, Bayesian methods can adapt to varying sample sizes.
This is a huge benefit for companies that want results faster.
Bayesian can help.
4. Handling Multiple Comparisons:
Bayesian methods naturally account for multiple comparisons without the need for complex corrections.
This is particularly advantageous in scenarios where multiple tests are being conducted simultaneously.
Bayesian methods naturally account for multiple comparisons without the need for complex corrections.
This is particularly advantageous in scenarios where multiple tests are being conducted simultaneously.
5. Quantifying Uncertainty:
Bayesian methods provide a direct measure of uncertainty in the estimates.
This includes not only estimating the most likely value of an effect but also the entire distribution of possible values, giving a fuller picture of the uncertainty.
Bayesian methods provide a direct measure of uncertainty in the estimates.
This includes not only estimating the most likely value of an effect but also the entire distribution of possible values, giving a fuller picture of the uncertainty.
🛑 Problem: 95% of data scientists (and data analysts) don't know how to apply Bayesian to A/B testing.
I have good news.
Over the past 4 weeks, I've been researching the A/B testing strategies that are used by MASSIVE internet companies like Booking.com, and...
I have good news.
Over the past 4 weeks, I've been researching the A/B testing strategies that are used by MASSIVE internet companies like Booking.com, and...
And I'm ready to share my results.
Attend my free A/B Testing workshop for Data Scientists:
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Attend my free A/B Testing workshop for Data Scientists:
👉 Register Here for R: us02web.zoom.us
👉 Register Here for Python: us02web.zoom.us
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