## Review: Basic Statistics

#### Basic Concepts

- Stats for
**characterizing collections** of something - Descriptive statistics
**Variables **or** constants**: instances
- A
**random variable** is a numerical outcome of an experiment in which we measure an entity
**Accuracy**, **precision, resolution** and **significant digits**: A result is considered accurate if it is consistent with the true or accepted value for that result. The precision of a result is an indication of how sharply it is defined. Accuracy is a measure of reliability, and is the difference between the True Value of a measured quantity and the Most Probable Value which has been derived from a series of measures. Precision is a measure of repeatability, i.e. the degree of agreement between individual measurements of a set of measurements, all of the same quantity. Resolution. This is the smallest interval measurable by an instrument.
**Independence **of datapoints, **Pseudoreplication**,
**Degrees of freedom**
- match
**Distributions **and** VariableTypes**
**Parametric statistics** make assumptions about distributions. They are more powerful as long as the asumtptions are not violated
- Central Tendency: Mean
- Dispersion: variance, standard deviation, standard error
- Shape: Kurtosis, Skewness

**Non-parametric statistics** do not make assumptions about the distribution.
- Central tendency: median
- Dispersion: minimum, maximum, range, quartiles, interquartile range, quantiles, percentiles

last modified: 3/12/08