Estimating population variance

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Why is population variance estimated to be $\frac{1}{N-1}\Sigma_{1 \leq i\leq N}(x_i-m)^2$ as opposed to sample variance which is $\frac{1}{N}\Sigma_{1 \leq i\leq N}(x_i-m)^2$, where m is the mean? I know that this is the unbiased estimate of population mean. But i am not able to grasp the intuition behind it. What does the term degree of freedom mean in this context?

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The comment made by Andre addresses the majority of your question. As to the rest, what does "degrees of freedom" mean, it is literally the number of free variables when you are computing whatever you are computing. if you calculate the sample variance without subtracting 1 from $N$ then you are assuming that you have one more degree of freedom than you actually have, because you are computing a number using the entire sample, which introduces an equation which reduces the number of degrees of freedom by 1.