Chapter 1 - Statistical & Critical Thinking Flashcards
Data
Collections of observations, such as measurements, genders, or survey responses.
Statistics
The science of planning studies and experiments; obtaining data, and then organizing, summarizing, presenting, analyzing, and interpreting those data and then drawing conclusions based on them.
Population
The complete collection of all measurements of data that are being considered.
Census
The collection of data from every member of the population.
Sample
A subcollection of members selected from a population.
Voluntary Response Sample
A sample in which the respondents themselves decide whether to be included.
Parameter
A numerical measurement describing some characteristic of a population.
Statistic
A numerical measurement describing some characteristic of a sample.
Quantitative
Data that consists of numbers representing counts or measurements.
Qualitative
Data that consist of names or labels that are not numbers representing counts or measurements.
Discrete Data
Data values that are quantitative and the number of values is finite or “countable”
Continuous Data
Result from infinitely many possible quantitative values, where the collection of values is not countable.
Nominal Level of Measurement
Characterized by data that consist of names, labels, or categories only.
Ordinal Level of Measurement
Data can be arranged in some order, but differences (obtained by subtraction) between data values either cannot be determined or are meaningless.
Interval Level of Measurement
Data can be arranged in some order and the differences between data values can be found and are meaningful. NO NATURAL ZERO!!
Ratio Level of Measurement
Data can be arranged in order, differences can be found and are meaningful, and there is a natural zero - ratios meaningful as well.
Observational Study
Study in which we observe and measure specific characteristics, but we don’t attempt to modify the subjects being studied.
Experiment
A treatment is applied and then we proceed to observe its effects on the subjects.
Simple Random Sample
A simple random sample of n subjects is selected in such a way that every possible sample of the same size n has the same chance of being chosen.
Random Sampling
Each member of the population has an equal chance of being selected.
Systematic Sampling
Select some starting point, then select every kth (such as every 50th) element in the population.
Convenience Sampling
Use results that are easiest to get.
Stratified Sampling
Subdivide the population into at least two different subgroups (or strata) so that subjects within the same subgroup share the same characteristics (such as gender or age bracket), then draw a sample from each subgroup.
Cluster Sampling
Divide the population into sections (or clusters), then randomly select some of those clusters, and then choose all members from those selected clusters.
Cross-Sectional Study
Data are observed, measured, and collected at one point in time, not over a period of time.
Retrospective Study
Data are collected from a past time period by going back in time (through examination of records, interviews, and so on.)
Prospective Study
Data are collected in the future from groups that share common factors (such groups are called cohorts).
Confounding
Occurs in an experiment when the investigators are not able to distinguish among the effects of different factors.
Sampling Error
Occurs when the sample has been selected with a random method, but there is a discrepancy between a sample result and the true population result; such an error results from chance sample fluctuations.
Nonsampling Error
The result of human error, including such factors as wrong data entries, computing errors, questions with biased wording, false data provided by respondents, forming biased conclusions, or applying statistical methods that are not appropriate for the circumstances.
Nonrandom Sampling Error
The result of using a sampling method that is not random, such as using a convenience sample or voluntary response sample.
