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There are strict rules that define when something is "statistically significant", it's not at all arbitrary. The problem is people thinking that just because so
by BeatLeJuce 8y ago
There are strict rules that define when something is "statistically significant", it's not at all arbitrary. The problem is people thinking that just because something is statistically significant, it is automatically true. Which it isn't. Statistically significance by definition includes the possibility that something was just a statistical oddity. This article essentially just reminds people of that, and urges them to abandon the "statistical significance is the same as ultimate truth" conclusion.
- neaden 8y agoThere aren't is the thing. There are widely used standards, but they don't actually have any real basis. Fisher I believe is the one who popularized it but it was for specific circumstances and he acknowledged that it was just a convenient thing.
- BeatLeJuce 8y agoSure there are. You can't just call a result "significant" at will. You can pull numbers out of thin air, pre-filter your data or carefully pick a statistical test to be in your favor. But it's still well-defined which outcomes you're allowed to call significant and which ones you can't.
- duxup 8y agoThank you.
- pryce 8y agoI feel like this misses another important prong of the article, which is: that failing to find a "statistically significant" correlation (according to some given significance test) is often mistakenly interpreted even by scientists themselves as meaning "We have a good basis to conclude that there is no such correlation".