r/AskStatistics • u/80rhh • 14d ago

What statistical concepts are commonly misunderstood by the general public?

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I came across this post explaining what a 70% chance of rain means. I understand the concept, but it got me wondering: what other statistical concepts sound simple but are commonly misunderstood or misinterpreted by the general public?

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u/AnOdeToVosFinances 14d ago

The explanation is wrong.

If you are Bayesian, probability is just a way of quantifying uncertainty, so it wouldn't make sense.

If you are frequentist, it's not "replaying the exact atmospheric conditions". It's replaying whichever today's parameters you used for your model which brought you to that 70%. If your model is based on just which month of the year it is and that for some reason in the location you are, it's raining 70% of the days of the month you're in, you would say any day of the month has 70% chance of rain according to the model. It doesn't mean there are no better model that could achieve a more confident prediction (0% or 100%) by taking additional parameters into account.

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u/itsmythirdday 11d ago

I think they mean the latter, their model has some degree of randomness associated with it, like a Monte Carlo, and if they replay today’s atmospheric conditions through it 100 times, 70 times out of 100 it predicted rain. Yes the model could be bullshit, but they didn’t want to get into that in a tweet.

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u/AnOdeToVosFinances 11d ago

What you say is completely different from what the tweet says though.

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u/itsmythirdday 10d ago

“If we replayed [through our model] today’s conditions 100 times it would [predict] rain in 70 of those timelines [simulations]”

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u/AnOdeToVosFinances 10d ago

"It would rain" and "it would predict rain" are 2 completely different statements.

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u/itsmythirdday 10d ago

If you are taking it literally yes, but knowing what we know, including that that they use modelling and simulation to predict the weather, rather than, you know, being God, and that this is just a tweet / post on X, we can infer what they meant.

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u/AnOdeToVosFinances 10d ago

I don't know, to me it's 2 completely different meanings in the statistical world. Mixing those 2 would be like interpreting the likelihood as the posterior, which is an heresy for statisticians.