liveBook · Manning

Non-significant difference

As explained in Effective Data Analysis

A non-significant difference is an observed difference for which the available statistical evidence does not cross the selected significance threshold.

A non-significant difference is an observed difference that does not cross the selected statistical significance threshold. It does not, by itself, establish that no relationship exists, that an opposite relationship exists, or that an intervention was ineffective. section 2.2.2

Interpret the result carefully by:

  • Checking whether the compared behaviors and outcomes were appropriate measures of the question. section 2.2.2
  • Considering more appropriate measures for future analyses when the current comparison is weak. section 2.2.2
  • Explaining to stakeholders that findings compatible with a hypothesis may still require more users, time, or data for confidence. section 2.2.2

Communicating the result

When a comparison produces no statistically significant relationship, avoid saying that the behaviors are unrelated. Explain what was measured, identify limitations in the comparison, and state whether additional data could clarify the finding. section 2.2.2

How Effective Data Analysis covers it

How deep it goes
Explains common misinterpretations and recommends reviewing measures, while stopping short of statistical calculation or threshold selection.
Examples
Uses comparisons of behaviors and outcomes, additional users or data, and stakeholder reactions to quantitative findings.
What you can do after
Evaluate non-significant findings without equating them with no relationship, opposite effects, or ineffective work.
How it connects
Connects hypotheses, statistical significance, stakeholder communication, quantitative findings, qualitative findings, and measurement choices.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.