P-Value and Confidence Interval: Differences & Examples

Statistical significance is the foundation of biostatistical empirical study whether it's evaluating the effectiveness of a new drug or understanding the relationship between variables in a public health study. Two concepts are fundamental to this analysis: p-values and confidence intervals. While they're often used together, they provide distinct yet complementary insights into data interpretation.

What is a P-value?

The P-value (or probability value) is a measure that helps to determine the statistical significance of experiment results in a hypothesis test that is if the null hypothesis were true, what is the probability that we would observe a result as extreme as or more extreme than the one we obtained. The smaller the p-value, the stronger the evidence against the null hypothesis.

Key Points:

•The null hypothesis (H₀) usually represents a statement of "no effect" or "no difference."
•A low P-value (typically < 0.05) suggests that the observed data is unlikely under the null hypothesis, and we may consider rejecting the null.
•A high P-value indicates that our observed result is consistent with the null hypothesis.

Suppose during the clinical trial of new drug the observed a P-value of 0.03 (p <0.05) would suggest there's only a 3% chance that the observed effect occurred due to random chance, assuming the drug had no real effect. This supports the idea that the drug might be effective.

Confidence Interval

A confidence interval provides a range of plausible values for a parameter based on sample data which means it gives us a range of values within which we are confident the true population parameter lies.

A 95% confidence interval is typically used which implies if an experiment is repeated 100 times, approximately 95% of the resulting confidence intervals would contain the true population parameter.

Key Points:

•CIs provide a range and not just a single point estimate, which makes them more informative and effective.
•A narrow CI suggests a more precise estimate, while a wider CI indicates more variability or uncertainty.
•Typically, the result is considered statistically significant if a CI for a difference does not include zero, or a CI for a ratio (like odds ratio or relative risk) does not include one.

P-value vs Confidence Interval: A Comparison

Feature	P-value	Confidence Interval
Nature	Single value	Range of values
Indicates	Statistical significance	Precision & significance
Interpretation	Probability under null hypothesis	Likely range of true effect
Useful for	Hypothesis testing	Estimating effect size

Several misconceptions persist around these statistical tools:

•P-values alone can be misleading. If the effect size is small, a statistically significant result (p < 0.05) may not have much practical significance. Likewise, a non-significant p-value doesn't necessarily mean "no effect" — it could simply reflect insufficient sample size.
•P-values don't measure the probability that the hypothesis is true.
•Confidence intervals don't indicate the probability that the parameter lies within the interval.
•Confidence interval statistical significance doesn't necessarily mean practical significance
•Statistical significance doesn't necessarily mean practical significance.
•Non-significant results don't prove the null hypothesis.

Calculate Confidence Level and P-values with BioStat Prime

BioStat Prime makes it simple to calculate and interpret Confidence Intervals (CI) and P-values, helping you draw accurate and meaningful conclusions from your data. With just a few clicks, you can:

Determine the precision of your estimates using CI
Assess statistical significance with clearly reported P-values
Choose from common thresholds (e.g., 0.05, 0.01) or set your own
Get guided outputs that assist in interpreting results with clarity

Whether you're testing hypotheses or comparing groups, BioStat Prime ensures your statistical analysis is both robust and easy to understand supporting sound scientific conclusions.

Conclusion

While both tools are useful, CIs are often considered more informative because they convey not only whether an effect exists, but how large it might be and how confident we are in the estimate.