Which statistical test should I use to compare two groups on a continuous outcome?

Use Welch's Independent t-test when comparing two separate groups of customers (e.g., Treatment vs. Control average order value), Paired t-test when measuring the same customers twice (before vs. after), or the Mann-Whitney U / Wilcoxon rank test when the data are heavily skewed or ordinal.

Which test compares conversion rates or yes/no outcomes between two groups?

Use the Two-Proportion A/B Test (or Chi-Square Test of Independence) when comparing independent groups (Ad A vs. Ad B click-through rate), and the McNemar Test when measuring paired binary choices on the same people before and after an intervention.

Which test should I use when comparing three or more groups?

Use One-Way ANOVA (or Kruskal-Wallis for skewed/ordinal data) when the outcome is continuous (e.g., spend across 4 customer tiers), and the Chi-Square Test of Independence when both variables are categorical.

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💡 Interactive Statistical Test Selector

Not sure which test to use? Follow this guided path to find the right statistical tool for your marketing question.

Each question narrows down your options based on your data structure and research goal.

Download a complete flowchart of all test selection paths as a study guide

Step 1

What are you measuring? (Your Outcome Variable)

💡 Help me understand "outcome variable"

The outcome (also called dependent variable or Y) is what you're trying to predict, explain, or measure. In marketing:

Numeric: Revenue, conversion rate (%), time on site, order value, satisfaction score
Binary: Did convert (yes/no), clicked ad (yes/no), churned (yes/no)
Categorical: Product chosen (A/B/C/D), segment membership (high/med/low)

Step 1b

Is your data time-ordered (sequential)?

💡 Time Series vs. Cross-Sectional Data

This determines whether you need time series methods or standard regression/comparison:

Time Series: Observations recorded in sequence over time (daily sales, weekly revenue, monthly KPIs). Past values may influence future values.
Cross-Sectional: Observations from different units at same time (customers, campaigns, products). Order doesn't matter.

Key question: Does the ORDER of your observations matter? Would rearranging rows change your analysis?

Step 2

How many predictors are you analyzing?

💡 Help me understand "predictors"

A predictor (also called independent variable or X) is what you think influences or explains the outcome. Examples:

One predictor: "Does ad spend affect revenue?" (X = spend, Y = revenue)
Multiple predictors: "What drives revenue: spend, seasonality, AND campaign type?" (X₁ = spend, X₂ = season, X₃ = campaign)

Tip: Start with one predictor to understand the basic relationship, then add more for modeling.

Step 3

What type of predictor do you have?

💡 Help me choose predictor type

Your predictor's type determines which test you'll use:

Numeric: Ad spend ($), days since last purchase, customer age, discount %
Binary groups: Control vs. Treatment, Email A vs. Email B, Before vs. After
3+ groups: Four email designs, three price points, five geographic regions

Step 4

How is your data structured?

💡 Independent vs. Paired data

This is about whether the same people/accounts appear in both groups:

Independent: Different customers in each group. Example: Group A gets Email 1, Group B gets Email 2 (different people)
Paired/Matched: Same customers measured twice. Example: Same customers see Offer A then Offer B, or before/after campaign

Key question: Can you draw a line connecting each observation in Group 1 to a specific paired observation in Group 2?

📋 At-a-Glance Statistical Test Reference & Calculators

Prefer to scan all common statistical tests side-by-side? Use this summary table to jump straight into the matching MktPraxis calculator.

Outcome (Y) & Predictor (X)	Statistical Test & Tool	Key Assumptions	Common Mistake to Avoid
Numeric Y + 1 Numeric X	Pearson Correlation / Simple Regression	Linear relationship, continuous variables, no extreme outliers	Assuming correlation implies causation
Numeric Y + 2 Independent Groups	Welch's Independent t-Test	Independent observations, approximately normal group distributions	Using when the same customers are measured twice
Numeric Y + 2 Paired Measurements	Paired t-Test	Matched pairs (before/after), pair differences approximately normal	Running an independent t-test and losing paired power
Numeric Y + 3+ Independent Groups	One-Way ANOVA	Independence, normality, similar group variances	Running multiple separate t-tests (inflates false positives)
Skewed / Ordinal Y + 2 or 3+ Groups	Rank-Based Nonparametric Tests	Ordinal or continuous outcome; robust to heavy skew and outliers	Using parametric means when a few whales dominate spend
Binary Y (Yes/No) + 2 Independent Groups	A/B Proportion Test	Independent visitors, random assignment, at least 10 conversions per arm	Stopping the test early ("peeking") when p dips below 0.05
Binary Y (Yes/No) + Paired Before/After	McNemar Test	Paired binary outcome on the same respondents at two time points	Using standard Chi-Square on repeated-measures data
Categorical Y + Categorical X	Chi-Square Test of Association	Independent observations, expected cell counts ≥ 5	Testing percentages instead of raw frequency counts
Numeric Y + Multiple Predictors	Multiple Linear Regression	Linearity, independent errors, homoscedasticity, low multicollinearity (VIF)	Comparing raw slopes across variables measured in different units
Binary Y (Yes/No) + Multiple Predictors	Logistic Regression	Binary outcome, independent observations, low multicollinearity	Interpreting log-odds or odds ratios as raw probability changes
Categorical Y (3+ Choices) + Multiple Predictors	Multinomial Logistic Regression	Mutually exclusive categories, independence of irrelevant alternatives	Forgetting that every coefficient is relative to the reference brand
Count Y (0, 1, 2, …) + Multiple Predictors	Count Data Regression (Poisson / NB2 / Hurdle)	Non-negative integer counts, check equidispersion ($\text{Var}(Y) \approx \mu$) vs. overdispersion or excess zeros	Fitting OLS to skewed counts or using Poisson SEs when overdispersion ($\phi \gg 1$) is present