14.7 Intro to Statistical Thinking

Common confounding factor

A(n) is the placebo effect, in which patients who think they are receiving a medication report an improvement (perceived or actual), even though the "treatment "they received was a placebo- a simulated or false treatment (sometimes called a "sugar pill)

Nonresponse

bias, in which individuals with a common characteristic are unwilling (or neglect) to respond to a questionnaire.

Statistical thinking

In , we expect a random sample to share approximately the same properties as the population.

Confidence level

The 95 % means that there is less than a 5 % chance (or 0.05 probability) that the result obtained from the sample could be obtained by chance alone.

Sample

A(n) is any subset of the population.

Statistical conclusions

are based on probability and are always accompanied by a confidence level.

Extraneous

or unintended variables that systematically affect the property being studied are called confounding variables.

Survey sampling

In , data are collected through responses to questionnaires.

Entire population

In statistical thinking, we make judgments about a(n) based on a sample.

Two variable data

In statistics, a question of interest when studying is whether or not the correlation is statistically significant.

Little correlation

If r is close to zero, the variables have .

Simple random sample

A(n) is one in which every individual in the population has the same probability of being selected.

bird watchers

In observational studies, data are collected by observation (such as when make their traditional Christmas bird count)

Random sample

A(n) is one that is selected arbitrarily and without bias.

importance of randomness

In selecting a sample, the can not be overemphasized.

In an experimental study, two groups are selected

a treatment group (in which individuals are given a treatment) and a control group (in which individuals are not given the treatment)