A p-value, or probability value, is a number between 0 and 1 which indicates the likelihood that results occurred by chance. Generally speaking, the smaller the p-value, the more solid the result.
A p-value of 0.04 can be read in any of the following ways:
Noise - or randomness - in data is inevitable.
Even in identical scenarios where both Group A and Group B perform equally, you can still expect small fluctuations in the data.
This presents a challenge.
If you can see a difference (in our example here: ±4%), at what point should you be concerned?
The p-value allows you to determine whether or not to believe the difference you are seeing.
The lower the p-value, the more confident you can be that the results are real. The greater the p-value, the more likely the results are coincidental (the effect of normal random noise), and hence, you should not draw any conclusions.
In the natural sciences, a p-value of less than 0.05, which is 5%, is generally agreed to be significant. This implies a less than 5% likelihood that results occurred by random chance, so conclusions are therefore deemed to be noteworthy. Similarly, a p-value less than 0.01, or 1%, is considered highly significant.
When considering impact-driven business decision, we need to add risk and reward. For example, a p-value of 0.15 may be acceptable if the cost of an initiative is low and the reward is high.
As a general approach, Pirical will highlight to clients where p-values are less than 0.10.
You will find p-values throughout Pirical On Demand where two populations are being compared.
P-values below 0.1 will be highlighted.
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