Population = All the observations that had a chance of being made when the sample of observations was taken.

Simple random sampleee = A sample selected in a way such that each member of the population has an equal chance of being picked.

Parameter = A single number that represents some characteristic of the entire population. Normally unobservable.

Statistic = A single number that represents a property of a bunch of numbers

Cases = The number of observations in the sample. Also called sample size. Normally indicated with the letter n.

Degrees of freedom = If we repeated the experiment, the number of independent decisions we could make.

Sample: a bunch of observations. consumable by the human brain either by computing a statistic or by drawing a graph. Attempts to answer the question, "What does this sample look like?".

Inferential statistics = Methods of estimating unobservable population parameters from observed sample statistics. Attempts to answer the question, "What might the population look like?".

Histogram = Observed distribution of the numbers in the sample. Shown as frequency of small ranges of values over the entire range of observations.

Probability distribution = Hypothesized or assumed distribution of the numbers in the entire population (an infinite number of numbers). Normally drawn in such a way that the area under the entire curve equals 1 (one). This means that the area under the curve between any two values is equal to the probability of observing a value in that range if a number is randomly selected from the population. It is also numerically equal to the percentage of the numbers in the population that lie in the range.

For example, if the area enclosed by the distribution between 50 and 60 is 0.23, it means that 23% of all the numbers in the population lie between 50 and 60. Alternately, if a number were to be randomly selected from that populatio; also, the probability of picking a number between 50 and 60 is 23%.

statistic = represents some characteristic of the sample. (what is normally observed.)

Robust statistic = A statistic that is not easily influenced by extreme observations.

Fickle statistic = A statistic that is easily influenced by extreme observations.

Uniform distribution = A distribution in which all values are equally likely (in a range from 0 to 1). The plot looks flat. There is no hump or central tendency.

Normal distribution (Also referred to as the z-distribution) A probability distribution described by the Gaussian density function. It is symmetric with a single mode and central tendency and is completely described by two parameters. These parameters corrrparameters correspond to the mean (m) and the standard deviation (s) of the population. m determines the location of the curve and s determines its shape.

Sampling distribution = A distribution of sample means.

Student's t-distribution = Another mathematical distribution similar to the normal distribution but with fatter tails. Sampling distributions follow the t-distribution when the exact shape (s) of the underlying population is not known and ts not known and the sample std dev, s, is used. It is described by three parameters - m, s and n, the degrees of freedom (= n-1).

z = the distance of an observation from mean of a normal distribution measured in the number of standard deviations.

t = the distance of an observation from m of a t-distribution measured in the number of standard deviations.

CLT = Regardless of the distribution of the underlying population, the sampling distribution approaches the normal distribution when the sample size is sufficiently large.