If you have ever read a research paper, chances are you’ve come across a sentence like:
“The results were statistically significant (p < 0.05).”
But what does that actually mean?
For many students and even professionals, the p-value feels mysterious, technical, and sometimes intimidating. Today, let’s break it down in simple, human terms — no complicated formulas, just understanding.
First, What Is a P-Value?
A p-value is a probability. It helps us answer one key question:
If there was really no effect or no difference, how likely is it that we would see results like this just by chance?
In research, we usually start with something called the null hypothesis. The null hypothesis simply says:
“There is no difference.”“There is no relationship.”“Nothing is happening.”
The p-value tells us how compatible our data is with that assumption.
Let’s Use a Real-Life Example
Imagine you are testing a new teaching method for biostatistics students. You divide students into two groups:
Group A: Old teaching method
Group B: New teaching method
After exams, Group B scores higher on average.
Now the big question is:
Is the new method truly better, or did this difference just happen by chance?
This is where the p-value comes in.
If the p-value is very small (commonly less than 0.05), it means:
“If there was truly no difference between the teaching methods, it would be very unlikely to see this big difference just by chance.”
So we reject the null hypothesis and say the result is statistically significant.
What P < 0.05 Really Means
When researchers say:
p < 0.05
They are saying:
There is less than a 5% probability that the observed results happened purely by random chance (assuming no real effect exists).
It does not mean:
The result is 95% true.
The hypothesis is 95% correct.
The intervention works 95% of the time.
This is one of the biggest misunderstandings in statistics.
Why 0.05?
The 0.05 threshold is mostly a convention. It was popularized by the statistician Ronald Fisher in the early 20th century.
There is nothing magical about 0.05. In some fields, researchers use:
0.01 (more strict)
0.10 (less strict)
It depends on how serious the consequences of being wrong are.
For example:
In drug trials, we often want very strong evidence.
In exploratory research, slightly weaker evidence may be acceptable.
Statistical Significance vs Practical Significance
Here is something very important:
A result can be statistically significant but not practically important.
Imagine a study with 50,000 participants finds that a new drug reduces blood pressure by 1 mmHg, with p < 0.001.
Statistically? Very strong evidence.Practically? That reduction may not matter clinically.
Statistics helps us detect differences. But professionals must interpret whether those differences matter in real life.
The Problem with Misusing P-Values
Over-reliance on p-values has caused major debates in science.
Some researchers:
Chase “significant” results.
Ignore effect sizes.
Avoid publishing non-significant findings.
Modern statistical thinking encourages us to look at:
Confidence intervals
Effect sizes
Study design quality
Reproducibility
The p-value should support thinking not replace it.
A Better Way to Think About It
Instead of asking:
“Is it significant?”
Ask:
How big is the effect?
Is it meaningful?
Is the study well designed?
Can the findings be replicated?
Statistics is not about proving something absolutely true. It is about measuring uncertainty in a structured, transparent way.
Final Thoughts
The p-value is not your enemy. It is a tool.
When used correctly, it helps researchers:
Make evidence-based decisions
Reduce bias
Quantify uncertainty
But like any tool, it must be understood and used responsibly.
Statistics is not just numbers it is a way of thinking.
And once you understand that, the fear disappears.

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