Ask about "the 5 rule in statistics" and you will get at least three different answers, because several unrelated conventions share the number. Here is each one, what it is for, and when it applies to business work.
1. The 5% significance level (the usual answer)
By convention, a result is called statistically significant when its p-value is below 0.05 - meaning there was less than a 5% chance of seeing a difference this large if there were genuinely no effect at all.
When to use it: as a default sanity threshold in tests and experiments.
What matters more than the rule: 0.05 is a convention, not a law of nature, and it says nothing about whether an effect is big enough to matter commercially. A statistically significant 0.2% conversion improvement may be worthless; a non-significant 6% improvement on a small sample may be well worth investigating with more data. Never let the threshold replace judgement, and never report a p-value without the effect size beside it.
2. The 5% sampling rule (finite population correction)
If your sample is more than about 5% of the total population, the standard formulas slightly overstate your uncertainty, and a finite population correction applies.
When it bites in business: constantly, and people miss it. Surveying 90 of your 500 customers is 18% of the population - your results are more precise than the textbook formula suggests. Surveying 900 of the UK's millions of consumers is a rounding error, and no correction is needed.
3. The rule of five (expected counts)
For chi-square tests and similar, a common guideline is that each expected cell count should be at least five, otherwise the test becomes unreliable.
When to use it: whenever you cross-tabulate categories - region by product type, for instance - and some combinations are rare. Combine sparse categories rather than trusting the test.
4. The informal "rule of five" for quick estimates
A rough sampling heuristic: with five random observations there is roughly a 93% chance that the true median lies between the smallest and largest of them. It is a genuinely useful reminder that even tiny samples carry information - though not enough to bet the business on.
Which one do people usually mean?
If a colleague says "is it significant at 5%?", they mean the first. If a textbook chapter on sampling says it, probably the second. If it comes up while you are testing whether two categorical variables are related, the third.
The underlying point
Every one of these is a convention designed to stop people over-claiming from limited data. Learning why each threshold exists is far more useful than memorising the number - and it is the difference between following rules and understanding them.
The Statistics for Business course covers significance, sampling and categorical tests with business examples throughout. Enquire today.