@tangming2005: 🧵 Why the obsession with p &lt...

🧵 Why the obsession with p < 0.05 is hurting science. A meme. A truth. A reality check.

1/
This meme says it all.
p = 0.0501? Pain.
p = 0.0499? Pure euphoria.
Two numbers. Nearly identical. Yet we treat them like night and day.
Why?

2/
The 0.05 p-value threshold is arbitrary.
It came from R.A. Fisher in the 1920s.
And we’ve been worshipping it like a sacred line ever since.
But it’s not magic. It's convention.

3/
What does p < 0.05 actually mean?
It means:
If the null hypothesis is true, there’s a 5% chance we’d see this extreme of a result by random chance.
That’s it.
Not: "This is true."
Not: "This will replicate."

4/
p = 0.0499 and p = 0.0501 are nearly identical.
But one gets you a “significant” label.
The other gets dismissed.
That’s broken thinking.

5/
Quoting Mike Love:
“A smaller p-value is not more interesting.”
“We should focus on effect sizes.”
He’s right.

6/
What’s an effect size?
It tells you how big the difference is.
Not just if it’s statistically detectable.
A gene with a log2 fold change of 3 matters.
Even if p = 0.06.

7/
P-values shrink with more data.
Got 10,000 samples?
You’ll find “significance” for even the tiniest differences.
Statistically significant ≠ Biologically meaningful.

8/
Also, be careful when testing thousands of genes.
Even with a p < 0.05 threshold, false positives will sneak in.
Use multiple testing correction: FDR, Bonferroni. Always.

9/
Let’s reframe:
Instead of:
“Did I beat the p < 0.05 line?”
Ask:
Is the effect meaningful?

Is it reproducible?

Does it make biological sense?

10/
Want a better practice?
Look at the distribution of p-values.

Report adjusted p-values (FDR).

Highlight effect sizes.

Don’t cherry-pick.

11/
And don’t forget confidence intervals.
They show the range of plausible effect sizes—not just a binary yes/no.
More context, more truth.

12/
Key takeaways:
0.05 is a line in sand, not a cliff

p-values ≠ effect size

Focus on biological meaning

Always correct for multiple testing

Use p-values as part of the story—not the whole story

13/
If you're making big decisions based on p = 0.0499 vs 0.0501...
You're not doing science.
You're doing stats theater.
Look deeper. Think harder. Go beyond the stars.

14/
And please—share this with a friend still chasing tiny p-values.
Let’s stop celebrating noise
and start celebrating insight.

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