Math-120- Vocab

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DATA:

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Collections of observations, such as measurements, genders, or survey responses.

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STATISTICS:

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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

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POPULATION:

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The complete collection of all measurements or data that are being considered

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CENSUS:

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The collection of data from every member of the population

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SAMPLE:

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A sub-collection of members selected from a population

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VOLUNTARY RESPONSE SAMPLE:

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One in which the respondents themselves decide whether to be included

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PARAMETER:

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A numerical measurement describing some characteristics of a population

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STATISTIC:

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A numerical measurement describing some characteristic of a sample

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QUANTITATIVE DATA:

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consists of numbers which represent counts or measurements

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CATEGORICAL DATA:

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consists of names or labels that are not numbers which represent counts or measurements

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DISCRETE DATA:

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values are quantitative and the number of values is either finite or “infinite‐but‐countable,” suchas1,2,3,…

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CONTINUOUS DATA:

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result from infinitely many possible quantitative values, where the collection of values is not countable (such as all the values between 1 and 2)

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NOMINAL LEVEL OF MEASUREMENT:

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Data that consist of names, labels or categoriesonly. The data cannot be arranged in an order, such as from high to low.

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ORDINAL LEVEL OF MEASUREMENT:

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Data that can be arranged in some order, but differences between data values either cannot be determined or are meaningless

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INTERVAL LEVEL OF MEASUREMENT:

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Data that can be arranged in order and differences between data values can be found and are meaningful. Furthermore, there is no natural zero starting point at which none of the quantity is present.

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RATIO LEVEL OF MEASUREMENT:

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Data that can be arranged in order, differences can be found and are meaningful, there is a natural zero or starting point at which none of the quantity is present. Furthermore, difference and ratios are both meaningful.

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OBSERVATIONAL STUDY:

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a study where we observe and measure specific characteristics, but we don’t attempt to modify the subjects being studied.

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EXPERIMENT:

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a study in which we apply some treatment and then proceed to observe its effects on the subjects. (Subjects in experiments are called experimental units.)

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LURKING VARIABLE:

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a variable that affects the variables included in the study, but it is not included in the study.

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SIMPLE RANDOM SAMPLE:

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a sample of n subjects selected in such a way that every possible sample of the same size n has the same chance of being chosen.

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SYSTEMATIC SAMPLING:

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a sample selected beginning at a starting point and then selecting every kth (for example, every 5th) element in the population.

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CONVENIENCE SAMPLING:

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a sample chosen because it is easy to obtain.

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STRATIFIED SAMPLING:

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a sample chosen by first subdividing the population into at least two different subgroups (or strata) so that subjects within the same subgroup share the same characteristics (such as age bracket). Then we draw a sample from each subgroup (or stratum).

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CLUSTER SAMPLING:

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a sample chosen by first subdividing the population into sections (or clusters), then randomly select some of those clusters and choose all the members from those selected clusters.

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RETROSPECTIVE STUDY:

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an observational study which collects data from a past time period (through examination of records, interviews, and so on.)

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CROSS‐SECTIONAL STUDY:

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an observational study where data is observed, measured and collected at one point in time, not over a period of time.

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PROSPECTIVE STUDY:

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an observational study where data are collected in the future from groups that share common factors (such groups are called cohorts).

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CONFOUNDING

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occurs in an experiment when the investigators are not able to distinguish among the effects of different factors.

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SAMPLING ERROR:

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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.

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NONSAMPLING ERROR:

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occurs from 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.

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NONRANDOM SAMPLING ERROR:

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the result of using a sampling method that is not random, such as using a convenience sample or a voluntary response sample.