[daniel-levin]

Agreed. I'm studying statistics at the moment and I'm continually reminded of how easy it is to choose the wrong model / distribution and be incorrect because of some non-obvious and technical reason. For example, just the other day, I wanted to use the binomial distribution to solve a problem. To use this distribution, the trials must be independent of one another. In that particular problem, there was a subtle condition that made the trials non-independent. I arrived at correct-appearing answers (0 <= P <= 1) that were actually all wrong. Statistics is way too easy to break to be used naively.

[lutusp]

> Statistics is way too easy to break to be used naively.

Fair enough, but the same argument could be made about using an unskewed standard distribution on non-symmetrical datasets, a common error even among people who should know better.

I think binomial functions should be included, on the ground that they're very useful and their probability of misuse is only equal to the continuous statistical forms, not more so.

[dandellion]

Hell, sometimes they use a dictionary when they should be using a list. Almost everything can be used wrongly by a begginer, which doesn't mean it shouldn't be there.

I think having a basic stats module always handy would be very convenient.

[lutusp]

> I think having a basic stats module always handy would be very convenient.

I absolutely agree. My only point was that these tools are sometimes misapplied, not at all to argue that they shouldn't be readily available. They should be.

[gknoy]

While you are correct, it sounds like his point was that even to someone moderately skilled it is easy to make a mistake that makes your work __completely__ invalid, rather than merely inefficient or too-complicated.