If you’ve ever read about medical studies or genetic research, you might have come across the term p-value. It sounds technical and a bit mysterious, but it’s actually a simple and powerful tool scientists use to determine whether their results are real or just chance. Let’s break it down together—no jargon, just plain English.
Table of contents
- Five key takeaways
- What is the p-value? Understanding the magic number behind scientific discoveries
- The null hypothesis
- The scientific “luck detector”
- How do scientists calculate the p-value?
- Genetics and health research
- Why p-values aren’t the whole story
- What can affect p-values?
- Why does the p-value matter to you?
- P-value FAQs
- References:
Five key takeaways
- The p-value is a number that helps scientists tell real results from random luck.
- A low p-value (below 0.05) means the findings are probably real and not just chance.
- A high p-value means the findings could easily be random.
- Scientists calculate p-values by comparing what they saw to what they’d expect if there was no real effect.
- In genetics and medicine, p-values help identify which genes or treatments truly affect health.
What is the p-value? Understanding the magic number behind scientific discoveries
The use of the p-value has increased over the last few decades [1] and has been used for over a century [2]. You’ll find p-values in almost all scientific studies [3]. Researchers and doctors use them to show whether there’s a real connection or difference between two groups for something they’re studying [3].
The null hypothesis
The p-value is a number that helps decide if we should reject or keep the “null hypothesis” (H0). The null hypothesis is the idea that there’s no real difference between the two groups for what’s being measured [4]. The “p” in p-value stands for probability.
A p-value tells us how likely it is to see the results we got—or even more extreme results—if the null hypothesis were true. In other words, it measures how strong the evidence is against the idea that there’s no difference [5]. The smaller the p-value, the stronger the evidence that a difference actually exists.
The scientific “luck detector”
Imagine you’re flipping a coin. You expect it to land on heads about half the time. Now, what if you flipped it 10 times and got heads 9 times? You’d probably think, “Hmm, is this just really lucky, or is the coin weighted somehow?”
The p-value is a number that answers a similar question in science. It tells researchers how likely it is to get their results if there were no real effect—in other words, if everything were just down to random chance.
- A low p-value (usually below 0.05) means it’s doubtful the results happened by chance. So, scientists feel confident that what they found is real and meaningful.
- A high p-value means the results could easily be random luck, so they’re less sure.
For example, in a study examining gene variants linked to type 2 diabetes, some p-values were as small as 0.00026. That’s way below 0.05, meaning there’s a very strong chance the link between those genes and diabetes isn’t just a coincidence—it’s real.
How do scientists calculate the p-value?
Calculating a p-value might sound like complicated math, but it’s really about comparing what you observed to what you’d expect to see if there was no real difference or effect.
Let’s go back to our coin toss at the fairground game. Normally, tossing a fair coin 100 times should give you about 50 heads and 50 tails. But what if you got 70 heads? You’d want to know: “How likely is it to get 70 heads out of 100 just by pure chance?”
To find out, scientists go through these steps:
- Set up a “null hypothesis”—this is the idea that there’s no real effect, like the coin being perfectly fair.
- Conduct the experiment and collect results—like tossing the coin 100 times and counting heads.
- Use statistics (formulas or computer simulations) to calculate the probability of getting results as extreme as, or even more extreme than, what they observed—assuming the null hypothesis is true.
If this probability—the p-value—is very low, it suggests the results probably aren’t just random luck.
Genetics and health research
When studying genetics, such as how certain gene variants (called SNPs) affect diabetes risk, researchers compare how often these variants occur in people with and without the disease [6].
They use statistical tests (such as the chi-squared test or logistic regression) to calculate the p-value, which tells them how surprising their results would be if the gene variant had no effect at all.
- A low p-value means the gene variant is likely playing a real role in increasing diabetes risk.
- A high p-value means the observed difference could be due to chance.
Why p-values aren’t the whole story
While p-values are helpful, they’re not perfect. They don’t tell you how big or significant a difference is—just whether it’s likely to be real [5], [7].
Also, the “cut-off” of 0.05 is a rule of thumb, not a hard line. Sometimes, a p-value just above 0.05 might still be important, especially in smaller studies [5].
Scientists also consider other factors, such as confidence intervals (which indicate a range within which the true effect likely lies) and the size of the study and potential errors [7].
What can affect p-values?
- Sample size: Bigger studies give more reliable p-values and can detect smaller differences.
- Random error: Small, unpredictable variations in data can make it harder to find real effects [2].
- Systematic error (bias): Mistakes in study design or data collection can wrongly suggest a difference or hide a real one [2], [3].
- Multiple comparisons: Testing many things at once increases the chance of finding a “false positive,” so adjustments are needed [8].
Why does the p-value matter to you?
Understanding p-values helps you make sense of headlines and health news. When scientists say their findings are “statistically significant,” they usually mean the p-value was low enough to be confident the results are real.
But remember: a low p-value doesn’t prove something for certain—it just shows strong evidence. Science is always about gathering more evidence and testing ideas again and again.
P-value FAQs
What is a p-value in simple terms?
A p-value is a number that helps scientists decide if their study results are real or just happened by chance. The lower the p-value, the more confident we are that the results are real and not just random luck.
Why do scientists use a cut-off of 0.05 for p-values?
A p-value below 0.05 is a common rule of thumb. It means there’s less than a 5% chance the results happened by accident. But it’s not a magic number—sometimes results just above 0.05 can still be important, especially in smaller studies.
Does a low p-value prove something is true?
No, a low p-value means there’s strong evidence for a real effect, but it doesn’t prove it for certain. Science is about building evidence over time, not just relying on one number.
What else do scientists look at besides the p-value?
Scientists also look at things like confidence intervals (which show a range for the real effect), the size of the study, and possible errors or bias. The p-value is just one piece of the puzzle.
Can p-values be affected by how the study is done?
Yes! The size of the study, random errors, bias, and even how many things are tested at once can all affect the p-value. That’s why good study design is so important.
Want to know more about genetics and health? Stay tuned to Medical Mojo for clear, friendly guides that make complex science simple and relevant to your life.
Disclaimer: This article is for informational purposes only and does not replace professional medical advice.
References:
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- These MS, Ronna B, Ott U. P value interpretations and considerations. J Thorac Dis. 2016 Sep;8(9): E928-E931. doi: 10.21037/jtd.2016.08.16. PMID: 27747028; PMCID: PMC5059270.
- Lang, J.M., Rothman, K.J. and Cann, C.I., 1998. That confounded P-value. Epidemiology (Cambridge, Mass.), 9(1), pp.7-8.
- Boos, D.D. and Stefanski, L.A., 2011. P-value precision and reproducibility. The American Statistician, 65(4), pp.213-221.
- O’Brien, S.F., Osmond, L. and Yi, Q.L., 2015. How do I interpret ap value?. Transfusion, 55(12), pp.2778-2782.
- Scott LJ, Bonnycastle LL, Willer CJ, Sprau AG, Jackson AU, Narisu N, Duren WL, Chines PS, Stringham HM, Erdos MR, Valle TT, Tuomilehto J, Bergman RN, Mohlke KL, Collins FS, Boehnke M. Association of transcription factor 7-like 2 (TCF7L2) variants with type 2 diabetes in a Finnish sample. Diabetes. 2006 Sep;55(9):2649-53.
- Gardner MJ, Altman DG. Confidence intervals rather than P values: estimation rather than hypothesis testing. Br Med J (Clin Res Ed). 1986;292(6522):746-50.
- Goodman SN. Multiple comparisons, explained. Am J Epidemiol. 1998;147(9):807-12.
