is a collection of methods for planning experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data.
is the complete collection of all elements (scores, people, measurements, and so on) to be studied.
can be separated into different categories that are distinguished by some nonnumeric characteristic.
result from either a finite number of possible values or a countable number of possible values. (That is, the number of possible values is 0, or 1, or 2, and so on.)
result from many possible values that can be associated with points on a continuous scale in such a way that there are no gaps or interruptions.
nominal level of measurement
is characterized by data that consist of names, labels, or categories only. The data cannot be arranged in an ordering scheme (such as low to high).
ordinal level of measurement
involves data that may be arranged in some order, but differences between data values either cannot be determined or are meaningless.
interval level of measurement
is like the ordinal level, with the additional property that we can determine meaningful amounts of differences between data. However, there is no inherent (natural) zero starting point (where none of the quantity is present).
ratio level of measurement
is the interval level modified to include the inherent zero starting point (where zero indicates that none of the quantity is present). For values at this level, differences and ratios are both meaningful.
we observe and measure specific characteristics, but we don't attempt to manipulate or modify the subjects being studied.
occurs when the effects from two or more variables cannot be distinguished from each other.
members of the population are selected in such a way that each individual has an equal chance of being selected.
simple random sample
of n subjects is selected in such a way that every possible sample of size n has the same chance of being chosen.
we subdivide the population into at least two different subpopulations (or strata) that share the same characteristics (such as gender), then we draw a sample from each stratum.
we select some starting point and then select every kth (such as every 50th) element in the population.
we first divide the population area into sections (or clusters), then randomly select a few of those sections, and then choose all the members from those selected sections.
is the difference between a sample result and the true population result; such an error results from chance sample fluctuations.