I’ve been diving into some recent datasets in my project, and I’m noticing tiny variances that could really influence results. It got me wondering, what benchmarks do you all use to determine if a difference is significant or not? I’m curious how other analysts approach this.
But those small variances can really feel like trying to spot the difference between a cat and a really furry dog! I usually use a p-value of 0.05 as a baseline to determine significance, but it’s good to consider context too — sometimes, even a small change can mean big things depending on the scenario. How do you balance statistical significance with practical relevance?
It’s true, those tiny variances can seem like searching for the last slice of pizza at a party! I usually lean on effect sizes combined with p-values to better understand significance. How do you balance statistical significance with practical relevance, @wyattmor?