Variance measures how much each score in a data set varies or deviates from the mean. It gives us an idea about how spread out our data points are.
Think of variance as checking whether students' test scores vary widely or not. If most students scored close to 90, there would be low variance because there isn't much difference between each student's score. However, if some students scored very high and others very low, there would be high variance because there is a wide range of scores.
Sum of squares: The sum of squares calculates how much each score deviates from the mean by squaring those differences and summing them up.
Population variance: Population variance is used when we have information about every member (individual) within a given population.
Sample variance: Sample variance is used when we only have information about a subset (sample) of individuals within a population.
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