Summary: P-values indicate whether results likely reflect real effects or random chance, with 0.05 threshold being conventional but arbitrary. Confidence intervals provide ranges of plausible effects—more informative than single estimates. Effect size measures how large effects are, distinguishing between statistically significant and practically important effects. Strong research evidence combines statistically significant p-values with reasonable effect sizes and narrow confidence intervals. Interpreting research correctly requires considering multiple statistical measures together rather than over-emphasizing any single statistic.
Understanding P-Values
A p-value measures whether study results likely reflect real effects or just random variation. Imagine a peptide produces no real effect, but you conducted the study anyway. Just by random chance, sometimes studies randomly show effects that don’t exist. A p-value answers: If the peptide has no real effect, how likely are we to observe these results anyway?
A small p-value means the observed results would be very unlikely if the peptide has no effect. This suggests the peptide probably does have a real effect. A large p-value means the observed results could easily happen by random chance even if the peptide has no effect. This suggests the peptide might not actually work.
The 0.05 Threshold
Researchers traditionally use 0.05 as the threshold for statistical significance. A p-value of 0.05 means there’s a 5% probability of observing these results if the peptide has no real effect. Most researchers consider this probability low enough to conclude the peptide probably works.
However, 0.05 is arbitrary. There’s nothing magical about this number—it’s a convention. Some researchers use 0.01 (1% probability) for stricter standards. Some accept 0.10 (10% probability) for more relaxed standards.
What P-Values Don’t Mean
A common mistake: thinking a p-value of 0.05 means there’s a 95% probability the peptide works. This is incorrect. P-values don’t tell you the probability of your hypothesis being true. They tell you the probability of observing your results if the null hypothesis is true.
Similarly, a statistically significant finding doesn’t mean the effect is large or important. A peptide might produce a statistically significant effect that’s too small to matter practically.
Confidence Intervals Explained
A confidence interval provides a range of plausible values for the true effect. Rather than saying “the peptide increases muscle by exactly 8 pounds,” research might say “we’re 95% confident the true effect is between 5 and 11 pounds.”
The 95% represents the confidence level—if we repeated the study many times, 95% of the time our confidence interval would contain the true effect. A wider interval (5 to 11 pounds) reflects more uncertainty than a narrow interval (7 to 9 pounds).
Confidence intervals are more informative than p-values alone because they show the range of possible effect sizes. A study might show a statistically significant effect with a confidence interval of 0.1 to 50 pounds—huge uncertainty about the actual effect. Another study might show statistical significance with confidence interval of 8 to 10 pounds—precise effect estimate.
Interpreting Confidence Intervals
If a confidence interval includes zero (like -3 to +7), the study can’t conclude the peptide has a consistent effect—the true effect might be positive, negative, or zero. If the interval is entirely above zero (like 5 to 11), the peptide probably produces consistent positive effects.
Effect Size and Practical Significance
Effect size measures how large an effect is, independent of sample size. A p-value tells you whether an effect exists; effect size tells you how big it is.
Imagine two studies. Study A: 50 people using peptide gain average 8 pounds; 50 people not using gain average 6 pounds. P-value = 0.04 (statistically significant). Study B: 1,000 people using peptide gain average 7.1 pounds; 1,000 people not using gain average 7 pounds. P-value = 0.02 (statistically significant).
Both reach statistical significance, but Study A shows a 2-pound difference while Study B shows a 0.1-pound difference. Study A’s effect is larger. Effect size quantifies this difference.
Practical Significance Versus Statistical Significance
A large enough study can show statistical significance for tiny effects. A peptide producing average 0.1-pound gain shows statistically significant with enough participants, but this effect is too small to matter practically.
This distinction matters enormously. Statistical significance doesn’t guarantee practical importance. A statistically significant effect might be irrelevantly small.
Common Effect Size Measures
Cohen’s d expresses effect size in standard deviation units. Effect size of 0.2 is considered small, 0.5 is medium, and 0.8 is large. This standardized measurement lets you compare effect sizes across different studies and measurements.
Relative Risk and Risk Reduction compare risk in treatment versus control groups. If 20% of treatment group experiences an outcome versus 10% of control group, the relative risk is 2.0 (twice the risk). The risk reduction is 10 percentage points.
Understanding which measure applies matters for interpreting results correctly.
Statistical Versus Clinical Significance
A treatment might be statistically significant but clinically insignificant. A peptide might reliably increase a blood marker but not improve how you actually feel or function. Distinguish between statistical proof of effect and real-world meaningfulness.
Consider your specific goal. If your goal is increasing a blood marker, statistical significance in that marker matters. If your goal is feeling better, a blood marker improvement is irrelevant unless it correlates with feeling better.
Sample Size Effects on Statistics
Sample size affects both p-values and confidence intervals. Large studies produce smaller p-values (easier to reach statistical significance) and narrower confidence intervals (more precise estimates).
This means large studies can show statistical significance for small effects, while small studies might fail to show significance for large effects. When evaluating research, larger sample sizes generally mean more reliable findings.
Misinterpretation Examples
Example 1: Research shows a peptide produces “statistically significant increase in muscle” with p=0.04 and effect size 0.3 (small). Effect exists but is small. Results don’t prove the peptide reliably produces dramatic muscle gains.
Example 2: Research shows no statistically significant difference (p=0.08) between peptide and control groups. Many interpret this as “the peptide doesn’t work.” Actually, it might work—the study just didn’t have enough power to detect the effect. Don’t confuse “not statistically significant” with “proven ineffective.”
Example 3: Confidence interval for effect is -5 to +15 pounds. This includes negative (peptide reduces muscle), near-zero (peptide has minimal effect), and positive effects. Results are inconclusive about the true effect.
Combining Information for Interpretation
Don’t rely on p-values alone. Combine p-values with confidence intervals and effect sizes. Strong evidence combines: p-value below 0.05, confidence interval not including zero, and reasonably large effect size.
Weak evidence might show p-value below 0.05 but tiny effect size, or p-value near 0.05 with wide confidence interval. Multiple statistics together paint a clearer picture than any single statistic.
