Sample statistics vs population statistics

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Sample statistics often differ from population statistics due to the inherent variability in sampling. The Central Limit Theorem suggests that larger sample sizes yield means closer to the population mean, while smaller samples may not accurately reflect population parameters. Non-normal distributions can invalidate the empirical rule, complicating the comparison between sample and population statistics. The distinction between sample and population statistics highlights that sample statistics are estimates, not exact values. Understanding these concepts is crucial for interpreting statistical data accurately.
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Homework Statement



My task is to explain why the sample statistics I have obtained differ from the population statistics I have obtained from some data - using "concepts taught in class, if they exist". I have calculated x̄ and s, as well as σ and µ.

Homework Equations



First of all, the distribution is not normal, thus the emperical rule is invalid.

The Attempt at a Solution



Part of me thinks it's a trick question because there are very few "concepts" I can think of. The only thing I can come up with is that the mean differs because it is merely one sample, and according to the Central Limit Theorum, if I had a bigger sample space, the mean would be similar. Similarly, the standard deviation differs because it is merely one sample. Is this all there is to it or am I missing something?
 
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Sample statistics are obtained by sampling from a population. The idea is that the statistical properties of a population can (usually) be only estimated. In this respect, I slightly doubt about your data-based \mu, \sigma^2 :-)
 
Question: A clock's minute hand has length 4 and its hour hand has length 3. What is the distance between the tips at the moment when it is increasing most rapidly?(Putnam Exam Question) Answer: Making assumption that both the hands moves at constant angular velocities, the answer is ## \sqrt{7} .## But don't you think this assumption is somewhat doubtful and wrong?

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