notation
(NL: notatie)
Sample and population
| Sample | Population | |
|---|---|---|
| Size | \(n\) | \(N\) |
| Mean | \(\overline{x}\) | \(\mu\) |
| Variance | \(s^2\) | \(\sigma^2\) |
| Standard deviation | \(s\) | \(\sigma\) |
Probability theory
See also probability
| Symbol | Interpretation |
|---|---|
| \(\Omega\) | universe |
| \(A, B, \ldots\) | events (with \(A \subset \Omega\), \(B \subset \Omega\), etc.) |
| \(P(A)\) | the probability of event \(A\) (with \(0 \leq P(A) \leq 1\)) |
| \(P(A|B)\) | the probability of \(A\) if \(B\) occurs |
The normal distribution
If a \(X\) has a normal distribution with expectation value \(\mu\) and standard deviation \(\sigma\), then we write \(X \sim \mathcal{N}(\mu, \ sigma)\).
We write the standard normal distribution as \(Z \sim \mathcal{N}(0, 1)\).
We write the probability distribution of the sample mean as \(M \sim \mathcal{N}(\mu, \frac{\sigma^2}{n})\).
- \(X \sim \mathcal{N}(\mu, \sigma)\)
- The stochastic variable \(X\) has a normal distribution with expected value \(\mu\) and standard deviation \(\sigma\)
- \(Z \sim \mathcal{N}(0, 1)\)
- The standaad normal distribution.
- \(M \sim \mathcal{N}(\mu, \frac{\sigma^2}{\sqrt{n}})\)
- The probability distribution of the sample mean (see the central limit theorem).
Statistic hypothesis tests
- \(H_0\) - the null hypothesis
- \(H_1\) - the alternative hypothesis
- \(\alpha\) - the significance level