Data
Collections of observations (such as measurements, genders, survey response)
Statistics
The science of planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data.
Population
The complete collection of all individuals (scores, people, measurements, and so on). To be studied. The collection is complete in the sense that it includes all of the individuals to be studied.
Census
The collection of data from every member of the population.
Sample
A subcollection of members selected from a population.
Parameter
A numerical measurement describing some characteristic of a population.
Statistic
A numerical measurement describing some characteristic of a sample.
Quantitative (or numerical) Data
Consist of numbers representing counts or measurements.
Categorical (or qualitative or attribute) Data
Consist of names or labels that are not numbers representing counts or measurements.
Discrete Data
Result when the number of possible values is either finite number or a "countable" number. (That is, the number of possible values is 0 or 1 or 2, and so on)
Continuous (numerical) Data
Result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps, interruptions, or jumps.
Nominal Level of Measurement
Is characterized by data that consists of names, labels, or categories only, the data cannot be arranged in an ordering scheme (such as low to high).
Ordinal Level of Measurement
If they can be arranged in some order, but differences (obtained by subtraction) between data valiues either cannot be determined or are meaningless.
Interval Level of Measurement
Is like the ordinal level, with the additional property that the difference between any two data values is meaningful. However, data at this level do not have a natural zero starting point (where none of the quantity is present).
Ration Level of Measurement
The interval level witht he additional property that there is also a natural zero starting point (where zero indicates that none of the quantity is present). For values at this level, differences and ratios are both meaningful.
Voluntary Response Sample
One in which the respondents themselves decide whether to be included.
Observational Study
We observe and measure specific characteristics, but we don't attempt to modify the subjects being studied.
Experiment
We apply some treatment and then proceed to observe its effects on the subjects.
Simple Random Sample
Of n subjects is selected in such a way that every possible sample of the sample size n has the same chance of being chosen.
Random Sample
Members from the population are selected in such a way that each individual member in the population has an equal chance of being selected.
Probability Sample
Involves selecting members from a population in such a way that each member of the population has a known (but not necessarily the same) chance of being selected.
Systematic Sampling
We select some starting point and then select every k-th (such as every 50th) element in the population.
Convenience Sampling
We simply use results that are very easy to get.
Stratified Sampling
We subdivide the population into at least twp different subgroups (or strata) so that subjects within the same subgroup share the same characteristics (such as gender or age bracket), then we draw a sample from each subgroup (or stratum).
Cluster Sampling
We first divide the population area into sections (or clusters), then randomly select some of those clusters, and then choose all the members from those selected clusters.
Cross-Sectional Study
Data are observed, measured and collected at one point in time. (Present)
Retrospective (or case control) Study
Data are collected from the past by going back in time (through examination of records, interviews, and so on). (Past)
Prospective (longitudinal or cohert) Study
Data are collected in the future from groups sharing common factors (called cohorts) (Future)
Confounding
Occurs in an experiment when you are not able to distinguish among the effects of different factors.
Sampling Error
The difference between a sample result and the true population result; such an error results from chance sample fluctuations.
Nonsampling Error
Occurs when the sample data are incorrectly collected, recorded, or analyzed (such as by selecting a biased sample, using a defective measurement instrument, or copying the data incorrectly).