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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.
17 Οκτ 2019 · The parameters $\mu$ and $\sigma$ of the normal distribution are called "Mittelwert" and "Standardabweichung" in German and their Greek letters have the phonetic equivalents "M" and "S" in Latin script.
20 Απρ 2016 · Understanding t-Tests: t-values and t-distributions. T-tests are handy hypothesis tests in statistics when you want to compare means. You can compare a sample mean to a hypothesized or target value using a one-sample t-test. You can compare the means of two groups with a two-sample t-test.
22 Μαΐ 2019 · In this case, the central limit theorem guarantees that if you compute the sample mean $$ \lim_{n\to\infty}\frac{X_1+\cdots+X_n}{n}, $$ this a priori random quantity will in fact converge (with probability $1$) to the theoretical mean $\mathbb EX_1$.
Depending on the t-test that you use, you can compare a sample mean to a hypothesized value, the means of two independent samples, or the difference between paired samples. In this post, I show you how t-tests use t-values and t-distributions to calculate probabilities and test hypotheses.
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.
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?
