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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?