A sample is that part of a population which is actually observed. In normal scientific practice, we demand that it be selected in such a way as to avoid presenting a biased view of the population. If statistical inference is to be used, there must be a way of assigning known probabilities of selection to each sample. If the probabilities of different samples are all equal, for example, the method is called simple random sampling. In statistics, a biased estimator is one that for some reason on average over- or underestimates what is being estimated. ... The topics below are usually included in the area of interpreting statistical data. ... In statistics, a simple random sample from a population is a sample chosen randomly, in which each member of the population has the same probability of being chosen. ...
See also: Sampling (statistics) Sampling is that part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a population of concern, especially for the purposes of statistical inference. ...
Samples from a continuous distribution may not have any repeated data values, so the mode is generally more informative with samples from discrete distributions.
The sample variance is the the average of the squared deviations of each sample value from the sample mean, except that instead of dividing the sum of the squared deviations by the sample size N, the sum is divided by N-1.
The sample interquartile range is the difference between the upper (75th percentile) and lower (25th percentile) quartiles of the data sample, which are the upper and lower bounds of the center half of the data values.
Feeds |
Contact