Frequentist vs Bayesian Statistics for Growth Marketers
Article written by
Stuart Brameld
When running growth marketing experiments, choosing the right statistical method is crucial. Two main approaches dominate the field: frequentist and Bayesian statistics. Understanding the differences between these methods can significantly improve how you interpret results and make decisions.
What Is Frequentist Statistics?
Frequentist statistics is the traditional method most marketers know. It focuses on the long-term frequency of events. In simple terms, it assumes that if you repeat an experiment many times, your results will eventually converge towards a true value.
With frequentist statistics, you typically:
Set a null hypothesis (no effect) and an alternative hypothesis (an effect exists).
Collect data and calculate a p-value.
Decide whether to reject or accept the null hypothesis based on the p-value.
While frequentist methods are straightforward and widely used, marketers often misunderstand p-values, leading to incorrect conclusions.
What Is Bayesian Statistics?
Bayesian statistics incorporates prior knowledge or beliefs into your analysis. Instead of relying solely on your current experiment's data, Bayesian methods combine existing knowledge (the prior) with new data to update your beliefs (the posterior).
Here is a quick comparison between frequentist and Bayesian statistics:
Aspect | Frequentist | Bayesian |
|---|---|---|
Interpretation of Probability | Long-run frequency of events | Degree of belief or certainty |
Use of Prior Knowledge | No prior knowledge used | Explicitly incorporates prior knowledge |
Output | P-values, confidence intervals | Posterior probabilities, credible intervals |
Decision Making | Binary (reject or accept hypothesis) | Probabilistic (degree of certainty) |
For a clear explanation, watch this short video by Cassie Kozyrkov: Frequentist vs Bayesian Statistics Explained.
Examples of Frequentist vs Bayesian in Growth Marketing
Landing Page A/B Test
Imagine testing two landing pages to see which generates more sign-ups. A frequentist approach involves running the test, calculating a p-value, and deciding if the difference is statistically significant. If the p-value is below your threshold (usually 0.05), you declare a winner.
A Bayesian approach starts with your prior belief about conversion rates (based on historical data). As new data arrives, you update your belief about which page performs better. Instead of a binary decision, you get a probability—such as "there is a 92% chance page B is better than page A". This is often more intuitive and actionable for marketers.
Email Subject Line Testing
Suppose you are testing two email subject lines. A frequentist method requires a fixed sample size and a clear stopping point. You wait until the test finishes before making a decision.
With Bayesian methods, you continuously update your beliefs as data arrives. This allows quicker decisions, saving time and resources. You might see early on that one subject line has a high probability of outperforming the other, allowing you to stop the test sooner.
Popular A/B Testing Tools and Their Statistical Methods
Most growth marketers rely on A/B testing tools. Here is a quick overview of popular tools and the statistical methods they use:
Tool | Statistical Method |
|---|---|
Frequentist and Bayesian (Hybrid) | |
Frequentist | |
Frequentist and Bayesian (Hybrid) | |
Bayesian | |
Frequentist |
How Growth Method Helps You Run Better Experiments
Choosing the right statistical approach is just one part of running effective growth marketing experiments. Growth Method is the only work management platform built specifically for growth marketers, helping you streamline your entire experimentation workflow.
With Growth Method, you can:
Ideate effectively: Our intuitive ideation system ensures your growth ideas align with team goals, follow hypothesis best practices, and are automatically categorised. Your entire team stays informed and can provide feedback easily.
Experiment faster: Growth Method enforces best practices from leading growth teams. Experiments move through clear stages—building, live, analysing, and complete—to increase velocity and learning.
Report effortlessly: Impress stakeholders with sleek, professional reports that clearly demonstrate your team's impact and ROI.
"We are on-track to deliver a 43% increase in inbound leads this year. There is no doubt the adoption of Growth Method is the primary driver behind these results." Laura Perrott, Colt Technology Services
Growth Method integrates seamlessly with major analytics platforms like Google Analytics, Amplitude, and MixPanel. Our AI-powered categorisation and experiment summaries save you valuable time. Plus, our free white glove migration service ensures a smooth transition.
Ready to optimise your growth marketing workflow? Growth Method helps companies implement a systematic approach to grow leads and revenue. Book a call today to see how we can help your team.
Article written by
Stuart Brameld