How to avoid failed statistics after solid experiments

Mar 02, 2026

“You must not fool yourself — and you are the easiest person to fool.”

— Richard Feynman

Dear Researcher,

For me, the most frustrating thing in the lab isn’t even failed experiments

It is:

Experiments that looked biologically solid—but the statistics were “non-significant”
Experiments that looked statistically “significant”—but turned out to be false positives (the most dangerous one)
Experiments that worked well on the bench were statistically significant, but reviewers pushed back on the analysis

Good news: you don’t always have to repeat the experiment

In many cases, the issue isn’t your experiment itself

It was that the statistical test had been chosen by default

There are 5 common statistical mistakes researchers keep repeating

Avoiding them fixes most (if not all) statistical problems

…and helps you present your work with confidence—knowing that you did the correct analysis

If this problem resonates, then Wildtype One is running its first-ever webinar

You’re hearing about it here first

Title:

Dead Salmon Stat Fails: How to avoid common mistakes and do correct statistics 🧬

We will cover:

The 5 statistical mistakes that most often lead to non-significant or fragile results
How to match your experiment to the right test
A simple 3-question framework you can use before submission

The session is:

This is the third good news:

No advanced mathematics or bioinformatics background is required

The session is built with young researchers in mind

It’s practical and focuses on preventing statistical issues before they happen

The webinar is done on a team basis (one lab group per session)

The booking is live now — and slots are available from March 16 to March 27

Each lab books one slot and invites its team

If this would be helpful for your group, you can book a time here: https://calendly.com/wildtypeone/stat-sig-webinar

Looking forward to seeing you live.

— Carl from Wildtype One 🧬

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