What Happens When p-Value is Less than 0.05?
Introduction
The p-value is a statistical concept used to determine the significance of a research finding. It represents the probability of observing the results of the study if the null hypothesis is true. The p-value is calculated using statistical tests, such as t-tests and confidence intervals, and is used to make inferences about the population parameters. However, the p-value alone is not enough to determine whether a study has found a statistically significant result. To make a decision, we need to look at the p-value in conjunction with other information, such as the sample size and effect size.
What is the p-value?
The p-value is calculated using the following formula:
p-value = 2 * [1 – Conclusion]
In other words, the p-value is the probability of observing the results of the study if the null hypothesis is true. A small p-value (less than 0.05) indicates that the results are statistically significant.
When is p-value LESS than 0.05?
In the context of research studies, a p-value of less than 0.05 is typically considered statistically significant. This means that there is a 5% chance of observing the results of the study if the null hypothesis is true. In other words, the results are significant enough to warrant further investigation or action.
What does a p-value of LESS than 0.05 mean in terms of the research question?
When a p-value is less than 0.05, it means that the research findings are significant enough to affect the conclusion. Here are some implications of a p-value of less than 0.05:
- Conclusion: If the p-value is less than 0.05, it suggests that the null hypothesis is rejected, which means that there is a statistically significant effect in the data.
- Testing: A p-value of less than 0.05 is not just a numerical value; it also represents the probability of observing the results of the study if the null hypothesis is true. This means that the null hypothesis can be rejected at a significance level of 5% or less.
- Conclusion of the study: A p-value of less than 0.05 indicates that the research findings are meaningful and worthy of further investigation.
Examples of p-value of LESS than 0.05
- Breast cancer study: A study found that the incidence of breast cancer in women who consumed high amounts of red meat was significantly higher than in women who consumed low amounts of red meat. The p-value of 0.014 indicates that the results are statistically significant.
- Exercise study: A study found that regular exercise reduced the risk of developing type 2 diabetes. The p-value of 0.012 indicates that the results are statistically significant.
When to be concerned about p-value of LESS than 0.05
While a p-value of less than 0.05 is often considered statistically significant, it is not the only metric to consider. Here are some situations where you may want to be concerned about a p-value of less than 0.05:
- Limited power: If the sample size is small, a p-value of less than 0.05 may not be convincing, even if the results are statistically significant.
- Inconsistent results: If multiple studies have produced statistically significant results, it may be due to publication bias or other factors that can affect the validity of the findings.
- Confounding variables: If there are confounding variables present in the study, a p-value of less than 0.05 may not be a reliable indicator of the research question.
Limitations of p-value
While a p-value of less than 0.05 is often considered statistically significant, it has several limitations:
- Type I error: A p-value of less than 0.05 does not guarantee that the null hypothesis is true; it only indicates that the results are unlikely under the null hypothesis.
- False positives: A p-value of less than 0.05 does not guarantee that a study has found a statistically significant effect; it may be due to a false positive.
- Publication bias: P-values may be affected by publication bias, which can lead to an inflated false positive rate.
Conclusion
In conclusion, a p-value of less than 0.05 is often considered statistically significant, but it is not the only metric to consider. Other factors, such as sample size, effect size, and research question, should also be taken into account when interpreting the results.