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