Data distributions

The keywords to memorise but not necessarilty understand are as follows:

• Normal Distribution
• This is a normal "bell curve" distribution.
• The bell curve is symmetrical and closely resembles the population as a whole
• Thus, the sample mean probably resembles the population mean.
• The standard deviation (sd) of the sample is likely to represent the (sd) of the population
• The more irregular-looking the curve, the less likely it is to represent the population
• Small samples and non-random sampling results in irregular curves
• Standard normal distribution
• This is a "z-transformation" of a normal distribution
• Z tranformation is the transformation of the points on a normal distribution into multiples of the standard deviation from the population mean. These multiples are called z values
• Data transformation
• If your data is skewed, you can use data transformation to make it resemble a normal distribution, for the purposes of performing some sort of statistical manipulation which requires a normal distribution.
• Binomial distribution
• This term describes the sample distribution of a binary variable (eg. alive/dead).
• The larger the sample,the closer the binomial distribution is to the normal distribution.
• Poisson distribution
• The probability of a number of events occurring per time period (or location)
• The events must be random and independent of each other.
• You only need prior knowledge of one parameter: the mean number of events per unit time (or per area of space)