In statistics, p-values are used to determine whether the results of a study are statistically significant. The p-value represents the probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is true. A very low p-value indicates that the observed results would be highly unlikely under the null hypothesis, which suggests that the null hypothesis should be rejected.
One common significance level that is used to determine statistical significance is p Definition of p-value
The p-value represents the probability of obtaining results at least as extreme as the observed results, assuming that the null hypothesis is true. The null hypothesis typically states that there is no real effect or no statistically significant difference between groups.
For example, consider a study that compares blood pressure in two groups of people using a new drug versus placebo. The null hypothesis would state that the drug has no effect on blood pressure compared to placebo. The p-value calculates the probability of observing a difference in blood pressure between the groups at least as large as the one observed, if there was truly no real effect of the drug.
The smaller the p-value, the less likely it is that you would get the observed results under the null hypothesis. A very low p-value suggests that the null hypothesis is unlikely, and should be rejected. This means the observed difference is statistically significant, and the effect is likely real.
What p
A p-value of 0.001 indicates that there is a 0.1% chance of observing results at least as extreme as the ones in your sample data, assuming the null hypothesis is true. This is an extremely low probability.
By convention, a p-value of 0.001 is considered highly statistically significant. It indicates very strong evidence against the null hypothesis, meaning the observed effect is highly likely to be real and not due to chance.
Comparison to other p-value thresholds
To interpret what p When to Use p
Researchers will often use a stricter p-value threshold like p Multiple comparisons
When a study involves doing many statistical tests on the data, the chance of getting a “false positive” result increases. Using a stricter significance level for individual tests, such as p Extraordinary claims
When a study is making an extraordinary claim that conflicts with previous knowledge, it requires stronger evidence. A significance level like p Policy/decision changes
In situations where study results will influence major policies, guidelines or clinical decisions, stricter standards of evidence are often required. A significance level like p Early stage research
When a topic is still new with limited research so far, results may be considered preliminary and exploratory. Using p Examples of When p
Here are some examples of research situations where a significance level of p Genetic studies
Due to testing many genetic variants, stringent significance levels are required in genome-wide association studies. P Safety/efficacy studies
For a pharmaceutical drug or medical device to be approved, the standard for evidence of efficacy is quite high. P Brain imaging
Neuroimaging studies often involve testing across many voxels in the brain. Using p Psychology research
Some controversial findings in psychology research have not held up to further replication. Using p Physics discoveries
The threshold for claiming discovery of new particles in physics is typically p Criticisms of Overly Strict p-values
While p Arbitrary threshold
There is nothing magical about 0.001 versus 0.01 or 0.05 – these cutoffs are arbitrary rules of thumb that have become convention. Setting hard thresholds can make researchers treat findings as black and white.
Reduces statistical power
Very low p-value cutoffs can make it harder to detect real effects that exist, but are smaller in size. This results in decreased statistical power.
Promotes publication bias
Journals are more likely to publish positive findings with p Not a measure of importance
Statistical significance does not necessarily mean an effect has any real-world importance. A tiny effect can reach p Multiple perspectives needed
Rather than a one-size-fits all rule, different fields may need different standards tailored to their research needs and maturity of knowledge.
Conclusion
A p-value of 0.001 indicates that there is only a 0.1% probability of getting results as extreme as observed, if the null hypothesis were true. By scientific convention, p
p-value Threshold Interpretation p Statistically significant, reasonable evidence against null p More statistically significant, stronger evidence against null p Highly statistically significant, very strong evidence against nullIn summary, while a p-value of 0.001 represents very strong statistical evidence, it should not be treated as absolute proof. The context of the research, specific analytical needs, and study limitations all need to be considered when carefully interpreting the meaning of results. Evaluating the broader impact and importance of findings is just as crucial as statistical tests.