Statistics

Alpha (α)

The false-positive threshold, why it is chosen before launch, and its link to power.

Alpha Spending

How sequential tests allocate false-positive risk across planned interim looks.

ANOVA

Comparing group means, interpreting the F-test, and when it fits multi-variant experiments.

Average

The arithmetic mean, when it can mislead, and how to interpret average experiment metrics.

Bayesian Inference

How priors and observed data produce posterior estimates for experiment decisions.

Bayesian vs Frequentist

Different questions, outputs, priors, stopping, and practical choice.

Bayes’ Theorem

The relationship between prior beliefs, new evidence, and updated probabilities.

Bernoulli Distribution

The probability model for one binary outcome such as a conversion.

Beta (β)

Type II error, its relationship to statistical power, and planning trade-offs.

Beta Spending

Managing false-negative risk and futility boundaries in sequential designs.

Bias

Sources of systematic error that can distort an experiment estimate or decision.

Binary Metric

Defining and analyzing outcomes with two states, such as converted or not converted.

Binomial Distribution

Success counts, assumptions, and why the distribution matters for conversion metrics.

Binomial Metric

A metric based on successes and opportunities, with a clearly defined denominator.

Bonferroni Correction

A conservative way to control false positives across multiple comparisons.

Bootstrap

Resampling observed data to estimate uncertainty when analytic formulas are difficult.

Causal Inference

Definition, assumptions, interpretation, and analysis context.

Causation

Definition, assumptions, interpretation, and analysis context.

Chi-Square Test

Definition, assumptions, interpretation, and analysis context.

Confidence Bound

Definition, assumptions, interpretation, and analysis context.

Confidence Interval

Formula intuition, uncertainty, width, and experiment reporting.

Confidence Level

Definition, assumptions, interpretation, and analysis context.

Confidence Threshold

Definition, assumptions, interpretation, and analysis context.

Consistent Estimator

Definition, assumptions, interpretation, and analysis context.

Continuous Metric

Definition, assumptions, interpretation, and analysis context.

Correlation

Definition, assumptions, interpretation, and analysis context.

Credible Interval

Definition, assumptions, interpretation, and analysis context.

Descriptive Statistics

Definition, assumptions, interpretation, and analysis context.

Distribution

Definition, assumptions, interpretation, and analysis context.

Efficient Estimator

Definition, assumptions, interpretation, and analysis context.

Error-Spending Function

Definition, assumptions, interpretation, and analysis context.

Estimator

Definition, assumptions, interpretation, and analysis context.

Expected Loss

Definition, assumptions, interpretation, and analysis context.

Expected Uplift

Definition, assumptions, interpretation, and analysis context.

External Validity

Definition, assumptions, interpretation, and analysis context.

False Discovery Rate (FDR)

Definition, assumptions, interpretation, and analysis context.

False Negative

Definition, assumptions, interpretation, and analysis context.

False Negative Rate (FNR)

Definition, assumptions, interpretation, and analysis context.

False Positive

Definition, assumptions, interpretation, and analysis context.

False Positive Rate (FPR)

Definition, assumptions, interpretation, and analysis context.

Family-Wise Error Rate (FWER)

Definition, assumptions, interpretation, and analysis context.

Fisher’s Exact Test

Definition, assumptions, interpretation, and analysis context.

Frequentist Statistics

Definition, assumptions, interpretation, and analysis context.

Holm–Bonferroni Correction

Definition, assumptions, interpretation, and analysis context.

Minimum Detectable Effect

Business value, sample-size trade-offs, and realistic MDE setting.

P-value

Correct interpretation, null hypothesis, common misreadings, and examples.

Statistical Power

Power, beta, sample size, MDE, and underpowered decisions.

Statistical Significance

Evidence, practical significance, confidence, and decision boundaries.

Fundamentals

Fundamentals

The conceptual foundation: what experiments are, how variants are compared, and how a testing program creates evidence.

CRO & funnels

CRO & funnels

Research-led optimization of the complete path from discovery to meaningful action.

Metrics

Metrics

Metric pages will connect formulas to denominators, business outcomes, guardrails, and experiment use cases.

Design

Design

Plan tests that can answer a useful question without creating avoidable validity risks.

Data quality

Data quality

Checks that determine whether an experiment result can be trusted before it informs a decision.

Implementation

Implementation

The delivery and measurement foundations behind reliable product and web experiments.

Product analytics

Product analytics

Concepts for understanding user behavior, product value, and the outcomes experiments are meant to improve.

Privacy

Privacy

Responsible measurement concepts for experimentation, marketing, and product analytics.