How to Choose Between One-Sided and Two-Sided Tests: A Complete Explanation

By Atticus Li, Head of Conversion Rate Optimization & UX at NRG Energy

Understanding the "Default Test Type" Mistake

Many experimenters fall into the trap of always using one-sided tests because they reach statistical significance faster (requiring approximately 20% less sample size). This approach is statistically inappropriate and can lead to serious business mistakes.

Let me elaborate on how you should select test types based on your hypothesis structure and risk tolerance:

Hypothesis-Based Selection

Your test type should directly match the nature of your hypothesis:

Directional Hypotheses

A directional hypothesis makes a specific prediction about the direction of effect:

• "Adding testimonials will increase conversion rate"

• "Simplifying the checkout flow will reduce abandonment"

• "The new pricing display will improve average order value"

For truly directional hypotheses, one-sided tests are appropriate because:

1. You're only interested in detecting effects in one specific direction

2. You've already established a theoretical or evidence-based reason to expect change in that direction

3. You would only implement the change if it shows improvement in that direction

Non-Directional Hypotheses

A non-directional hypothesis simply predicts a difference without specifying direction:

• "Changing the navigation menu will affect user engagement"

• "The new product recommendation algorithm will impact conversion rate"

• "Redesigning the dashboard will change user retention"

For non-directional hypotheses, two-sided tests are required because:

1. You're genuinely uncertain about which direction the effect might go

2. You need to detect significant effects in either direction

3. The implementation decision might depend on detecting effects in either direction

Risk Tolerance as a Decision Factor

Your organization's risk tolerance should heavily influence your test type selection:

Low Risk Tolerance Scenarios

Use two-sided tests when:

• Testing changes to core business functionality where negative impacts would be costly

• Evaluating features that could affect brand perception or user trust

• Running tests on high-traffic/high-value pages where mistakes have large consequences

• Testing with small sample sizes where you need maximum confidence in results

Moderate Risk Tolerance Scenarios

The decision becomes more nuanced:

• For incremental changes with strong directional evidence: one-sided may be appropriate

• For more significant changes where you'd still want to know about negative effects: two-sided

Higher Risk Tolerance Scenarios

One-sided tests might be appropriate when:

• Running exploratory tests where you're only looking for potential wins

• Testing minor UI changes that are unlikely to harm the experience

• When you have very limited traffic and need to maximize statistical power

• When you're following up on previously successful tests with refinements

Practical Decision Framework

Here's a practical framework to decide which test to use:

1. Start with your hypothesis - Is it genuinely directional with strong prior evidence?

   • If YES → continue to question 2

   • If NO → use a two-sided test

2. Consider implementation criteria - Would you only implement if there's improvement?

   • If YES → continue to question 3

   • If NO → use a two-sided test

3. Evaluate downside risk - How important is it to detect negative impacts?

   • If VERY IMPORTANT → use a two-sided test

   • If LESS CRITICAL → continue to question 4

4. Assess communication context - Will you need to defend results to skeptical stakeholders?

   • If YES → use a two-sided test (more conservative)

   • If NO → a one-sided test may be appropriate

Real-World Example

Scenario: You're testing a new product recommendation algorithm.

Hypothesis Analysis:

• If your hypothesis is "The new algorithm will increase conversion rate" based on strong prior data → potentially one-sided

• If your hypothesis is "The new algorithm will affect user behavior" without strong directional evidence → two-sided

Risk Assessment:

• If a potential decrease in conversions would be extremely costly → two-sided

• If you're primarily exploring new approaches and would only implement with positive results → potentially one-sided

Implementation Decision:

• If you would only implement with a conversion increase → potentially one-sided

• If you might implement even with mixed results (e.g., conversions slightly down but AOV up) → two-sided

Best Practice for Documentation

Always document your test type decision before running the experiment:

• Explicitly state your hypothesis and its direction

• Document your reasoning for choosing one-sided or two-sided

• Specify the metrics that will determine success

• Note your required significance level (typically p < 0.05)

This documentation protects against the temptation to change test types after seeing initial results, which is a form of p-hacking and invalidates your statistical inference.

Remember: The goal isn't to reach statistical significance faster—it's to make the right business decisions based on valid statistical inference.