- Start with the size of the difference
- Use an invented example
- Find the effect size and interval
- Ask about practical trade-offs
- Beware of threshold thinking
- Translate it for a family decision
- Keep health choices proportionate
- Sources and further reading
- Put the size of the difference into ordinary language
- A practical-importance checklist
A study can report a “statistically significant” result even when the difference is tiny. The phrase has a technical meaning: under the study’s assumptions, the observed pattern would be less compatible with random sampling variation if there were no underlying difference. It does not mean large, important or guaranteed.
Start with the size of the difference
Ask what actually changed. Did children gain ten points on a meaningful assessment, or a fraction of a point on a long scale? Did the change last? Could families notice it in daily life?
The Office for National Statistics explains that significance testing helps distinguish likely population changes from random sampling variation. It also deliberately says “statistically significant” to avoid confusing the term with ordinary importance.
Use an invented example
Imagine an app produces an average improvement of 0.4 points on a 100-point score. A very large sample might make that difference statistically significant. But parents still need to know whether 0.4 points matters, whether the score measures something useful and what the app costs in time, money or data.
Find the effect size and interval
The effect size describes how large a difference or relationship is. A confidence interval shows a range of values compatible with the data and model. A narrow interval around a tiny effect suggests precision about a small difference; a wide interval may include effects that range from negligible to meaningful.
Read the confidence interval guide before treating one threshold as a yes-or-no verdict.
Ask about practical trade-offs
- Does the outcome matter to children and families?
- How much time and effort does the change require?
- Are there costs, accessibility barriers or unintended harms?
- Does the benefit persist after the study ends?
- How many children improved, stayed the same or did worse?
Beware of threshold thinking
A result just below a conventional significance threshold is not necessarily worthless, and one just above it is not automatically convincing. Check the design, data quality, analysis plan and wider evidence. Repeated testing of many outcomes can also produce chance findings unless researchers account for it.
Translate it for a family decision
Try: “The study found a difference unlikely to be explained by sampling variation alone, but the average change was small. We need to decide whether that size matters for us.” This preserves the evidence without inflating it.
When the headline uses relative percentages, combine this article with the absolute-versus-relative risk guide.
Keep health choices proportionate
Do not start or stop treatment, supplements or safety practices because of a p-value. Ask a qualified health professional about absolute benefits, harms and relevance to your child. For everyday low-risk parenting ideas, a small, reversible trial may be reasonable, but it is not proof that the study applies to everyone.
Sources and further reading
Put the size of the difference into ordinary language
Suppose a study reports that two groups differed by a small fraction on a long questionnaire. A low p-value may make that difference statistically significant, especially in a very large sample. The family question is different: would anyone notice the change, does it last, and is it worth the time, cost, or disruption?
Look for the actual numbers in each group, the units used, and a confidence interval around the estimate. A narrow interval suggests greater precision; a wide one leaves more uncertainty. Also ask whether researchers measured something families care about directly or used a convenient stand-in.
A practical-importance checklist
- Magnitude: How large is the difference in familiar units?
- Duration: Was it observed briefly or sustained?
- Trade-offs: Were effort, cost, side effects, or unequal access considered?
- Fit: Did the study include children like yours and settings like yours?
- Consistency: Do other well-designed studies point the same way?
For a low-risk idea, a modest possible benefit may justify a gentle trial. For a costly, restrictive, or health-related change, ask for stronger and more directly relevant evidence. Statistical significance can be one useful signal, but family decisions need the whole picture.
