- Significance means a result is unlikely under no effect.
- It doesn’t measure size or importance.
- Effect size and confidence intervals matter more.
- Many outcomes tested raise the chance of false positives.
What a p-value is
A p-value estimates how likely it is to see a result at least as large as the one observed, if the treatment truly had no effect. By convention, p below 0.05 is called “statistically significant”. It means the result would be fairly unusual if there were no effect, not that the effect is certain, large or important.
Significant vs meaningful
| Question | What answers it |
|---|---|
| Is the result unlikely to be chance? | Statistical significance (p-value) |
| How large is the effect? | Effect size |
| How precise is the estimate? | Confidence interval |
| Would it matter to people? | Clinical or practical importance |
Effect size
Effect size describes how big the difference is between groups, for example a change in a hormone level or in strength. A tiny difference can be statistically significant in a large study, and a large difference can fail to reach significance in a small one. Effect size, not the p-value, tells you whether something is worth caring about.
Confidence intervals
A 95% confidence interval gives a range of plausible values for the true effect. A narrow interval means a precise estimate; a wide one means a lot of uncertainty. An interval that only just excludes zero suggests the effect could be very small. Small shilajit trials tend to have wide intervals.
The multiple comparisons problem
If a study measures many outcomes, some will reach significance by chance alone. With 20 outcomes tested at p < 0.05, about one would be expected to look significant even if nothing worked. Good studies name a primary outcome in advance and adjust for multiple tests. Be cautious when a study reports one significant result among many measured; see what research measures.
Significant changes in markers
A significant change in a blood marker, such as a hormone level, doesn’t necessarily mean people feel better or are healthier. For example, a rise in total testosterone within the normal range may not change how someone feels. Ask whether the outcome is something that matters in daily life; see testosterone.
“Not significant” doesn’t mean “no effect”
A non-significant result in a small study may simply mean the study was too small to detect an effect; see sample size. Equally, it isn’t evidence of benefit. It means the question remains open.
How significance is used in marketing
- “Significantly increased”Statistically significant, perhaps small.
- Percentage changes without contextA 20% rise from a low starting point may be small in absolute terms.
- Cherry-picked outcomesThe one significant result among many.
Putting it into practice
When you read “significant”, look for three numbers: the size of the difference, its confidence interval and the p-value. Then ask whether the outcome matters in practice and whether it was the study’s main outcome. That turns a buzzword into an informed judgement; see how to read a study.
A worked example
Study A: a 2% difference in strength, p = 0.03, in 2,000 people. Significant, but the effect is small.
Study B: a 15% difference, p = 0.08, in 30 people. Not significant, but the effect might be large; the study was too small to tell.
Neither result alone tells you whether the effect is real and worthwhile. Both need context and replication.
Why pre-registration helps
When researchers register their main outcome before starting, it becomes harder to highlight whichever result happened to reach significance. A registered primary outcome that reaches significance is more convincing than one significant result found among many after the fact.
In short
“Significant” is a statistical word, not a verdict on importance. Look past it to the size of the effect, the uncertainty around it and whether it matters in everyday life. Then look for replication before drawing conclusions.
Where to find these numbers
Effect sizes, confidence intervals and p-values usually appear in a study’s results tables, not its abstract. If a paper gives only p-values without the size of the difference, that is itself a limitation worth noting. Our evidence pages describe effect sizes where studies report them. When they don’t, we say so, so that you can judge how much a result really means rather than relying on the word “significant” alone. Context always matters more than a single number.
The bottom line
Statistical significance says a result is unlikely to be chance alone. It doesn’t say the effect is large, important or reliable. For shilajit research, look at effect sizes, confidence intervals and whether the outcome matters, and remember that one significant result needs replication.
Frequently asked questions
What does statistically significant mean?
That a result would be fairly unusual if there were really no effect, usually a p-value below 0.05.
Does significant mean important?
No. Importance depends on the size of the effect and whether it matters in practice.
What is an effect size?
How large the difference between groups is.
What is a confidence interval?
A range of plausible values for the true effect.
What is the multiple comparisons problem?
Testing many outcomes makes some look significant by chance.
Does “not significant” mean no effect?
No. A small study may simply have been unable to detect it.
Is a significant hormone change a real benefit?
Not necessarily; markers may change without noticeable effects.
What should I look for instead of “significant”?
Effect size, confidence interval and whether the outcome matters.