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I'll use $\sigma$ for SD to avoid confusion with SSD: If $\sigma = (\frac{SSD}{n-1})^{0.5}$, then $\sigma^2 = \frac{SSD}{n-1}$. Therefore: $SSD = \sigma^2 (n-1)$


A sample cannot be normal because it is finite, so it has a minimum and maximum. A true normal distribution has no such bounds and ranges from $-\infty$ to $\infty$. (There are other arguments for why a sample cannot be normal, and my explanation is a bit superficial, but that is the most straightforward way to describe it, I believe.) While the sample ...


Two points here. First, as regards an "infinite" population, you are really referring to a data generating process, one which can produce an infinite number of potential (think "future" if it helps to conceptualize) female responses, and one which can produce an infinite number of human responses. In this setting, the fact that female ...

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