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29 Δεκ 2023 · In summary, the key differences between the two mean formulas are µ vs. x̅ (mu vs. x bar symbols) and N vs. n. In each case, the former relates to the population, while the latter is for the sample mean formula.
22 Μαΐ 2019 · The mean is computed mathematically, by integrating against the probability density function. Thus, both the variable $X$ and the mean $\mu=\mathbb EX$ are theoretical quantities. They describe the statitician's model of the quantity of interest.
25 Ιουν 2023 · μ = ( Σ Xi ) / N. Here, Σ Xi represents the sum of all scores present in the population, while N denotes the total number of individuals or cases within the population. The population mean is calculated by summing up all the scores in the population and dividing by the total number of individuals or a case in statistics.
The utility of using $\mu$ is mostly in situations where you want to take advantage of this loose connection. The two classic examples are when it is a parameter, e.g. in $N(\mu,\sigma^2)$, and when you want to talk simply about an estimate, e.g. $\hat{\mu}$.
What is the difference between µ when being the population mean, and µ when being the mean or the expected value? What confuses me is that the same letter is being used to describe two different metrics- or are they?
One-sample: Compares a sample mean to a reference value. Two-sample: Compares two sample means. Paired: Compares the means of matched pairs, such as before and after scores. In this post, you’ll learn about the different types of t tests, when you should use each one, and their assumptions.
In this one, you’ll understand when to use the T-Test, the different types of T-Test, math behind it, how to determine which test to choose in what situation and why, how to read from the t-tables, example situations and how to apply it in R and Python.
