The article highlights the challenges of interpreting frequentist statistics, including confidence intervals and p-values, as even professionals struggle with these concepts. In contrast, Bayesian statistics provides more intuitive insights that align with common beliefs in industry. While many tools are incorporating Bayesian methods, practitioners still need to adapt to this shift away from traditional frequentist analysis. The article introduces a practical guide to creating a Bayesian A/B testing tool, emphasizing that the process is accessible and rewarding, with a focus on enhancing experimentation accuracy in various fields.
Even professional researchers struggle to interpret confidence intervals and p-values; Bayesian statistics can provide clearer insights than frequentist analyses.
Many believe frequentist conclusions like 'there's a 95% chance the true uplift lies between X% and Y%' could be better articulated through Bayesian approaches.
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