26 terms

All the definitions from chapter 1 of Elementary Statistics the 2nd Canadian Edition by Mario Triola.

Statistics

is a collection of methods for planning experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data.

population

is the complete collection of all elements (scores, people, measurements, and so on) to be studied.

census

is the collection of data from every element in a population.

sample

is a subcollection of elements drawn from a population.

parameter

is a numerical measurement describing some characteristic of a population.

statistic

is a numerical measurement describing some characteristic of a sample.

Quantitative data

consist of numbers representing counts or measurements.

Qualitative data

can be separated into different categories that are distinguished by some nonnumeric characteristic.

Discrete data

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.)

Continuous data

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.

self-selected survey

is 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 manipulate or modify the subjects being studied.

experiment

we apply some treatment and then proceed to observe its effects on the subjects.

Confounding

occurs when the effects from two or more variables cannot be distinguished from each other.

random sample

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.

stratified sampling

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.

systematic sampling

we select some starting point and then select every kth (such as every 50th) element in the population.

cluster sampling

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.

convenience sampling

we simply use results that are readily available.

sampling error

is 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 nonrandom and biased sample, using a defective measuring instrument, or copying the sample data incorrectly).