// Technical insights
From data to understanding
Practical notes on statistics, experimental design, visualization and reproducible research — written for biologists and researchers working with complex data.
The 5 statistical mistakes that quietly ruin scientific papers
Pseudoreplication, p-hacking, correlation versus causation, violated assumptions and misleading p-values: five mistakes that continue to undermine scientific conclusions.
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Topics currently in production for the blog and YouTube channel.
Your bar graphs are lying to you — here's why
Means ± SEM can hide the most important information in your data. What to show instead.
How to read a volcano plot without fooling yourself
Effect size, significance and the traps hidden in differential expression figures.
What a confidence interval actually means
One of the most misunderstood concepts in all of statistics.
Your UMAP is lying to you
Why clusters appear, disappear and move depending on arbitrary choices.
Experimental design mistakes to avoid before collecting data
The most expensive statistical errors happen before the experiment even starts.
Batch effects: the invisible problem that invalidates your analysis
How technical variation masquerades as biology and silently drives false discoveries.
Bayesian vs frequentist statistics: which should biologists use?
A practical comparison without the philosophical wars.
How to choose the right statistical test
A decision framework that goes beyond the usual flowcharts.