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I disagree with a lot of the answers advocating people to purely think of variance as spread. As smart people (Nassim Taleb) have pointed out, when people think of variance as spread they just assume it is MAD.

Variance is a description of how far members are from the mean, AND it judges each observation's importance by this same distance. This means observations far away are judged more importantly. Hence squares.

I think the variance of a continuous uniform variable is the easiest to picture. Each observation can have a square drawn to it. Stacking these squares creates a pyramid. Cut the pyramid in half so half the weight is in one side and half is in the other. The face where you cut it is the variance.

I disagree with a lot of the answers advocating people to purely think of variance as spread. As smart people (Nassim Taleb) have pointed out, when people think of variance as spread they just assume it is MAD.

Variance is a description of how far members are from the mean, AND it judges each observation's importance by this same distance. This means observations far away are judged more importantly. Hence squares.

I think the variance of a continuous uniform variable is the easiest to picture. Each observation can have a square drawn to it. Stacking these squares creates a pyramid. Cut the pyramid horizontally in half so half the weight is on the upper half and half is in the lower side. The height where you cut it is the variance.

I disagree with a lot of the answers advocating people to purely think of variance as spread. As smart people (Nassim Taleb) have pointed out, when people think of variance as spread they just assume it is MAD.

Variance is a description of how far members are from the mean, AND it judges each observation's importance by this same distance. This means observations far away are judged more importantly. Hence squares.

I think the variance of a continuous uniform variable is the easiest to picture. Each observation can have a square drawn to it. Stacking these squares creates a pyramid. Cut the pyramid in half so half the weight is in one side and half is in the other. The face where you cut it is the variance.

I disagree with a lot of the answers advocating people to purely think of variance as spread. As smart people (Nassim Taleb) have pointed out, when people think of variance as spread they just assume it is MAD.

Variance is a description of how far members are from the mean, AND it judges each observation's importance by this same distance. This means observations far away are judged more importantly. Hence squares.

I think the variance of a continuous uniform variable is the easiest to picture. Each observation can have a square drawn to it. Stacking these squares creates a pyramid. Cut the pyramid in half so half the weight is in one side and half is in the other. The face where you cut it is the variance.

I disagree with a lot of the answers advocating people to purely think of variance as spread. As smart people (Nassim Taleb) have pointed out, when people think of variance as spread they just assume it is MAD. A serious number of statistically fluent peers "fudge" it. It's needless.

Variance is a description of how far members from the mean, AND it judges each observation's importance in deciding this by how far away it is. This means observations far away are judged more importantly. Hence squares.

I think the variance of a continuous uniform variable is almost easier to picture than any other. Each observation can have a square drawn to it. Stacking these squares creates a pyramid. Cut the pyramid in half so half the weight is in one side and half is in the other. The face where you cut it is the variance.

I disagree with a lot of the answers advocating people to purely think of variance as spread. As smart people (Nassim Taleb) have pointed out, when people think of variance as spread they just assume it is MAD.

Variance is a description of how far members are from the mean, AND it judges each observation's importance by this same distance. This means observations far away are judged more importantly. Hence squares.

I think the variance of a continuous uniform variable is the easiest to picture. Each observation can have a square drawn to it. Stacking these squares creates a pyramid. Cut the pyramid in half so half the weight is in one side and half is in the other. The face where you cut it is the variance.