AP Statistics - Unit 1 terms

Statistics

the collection of methods to plan studies and experiments, along with methods to collect, organize, summarize, present, analyze, interpret, and draw conclusions.

Population

the complete collection of all subjects to be studied.

Census

Tally of the entire population

Sample

Subset of population

Parameter

Numerical measurement describing some characteristic of a population

Statistic

Numerical measurement describing some number of a sample.

Quantitative

Data that consists of numbers with measurement meaning (ex. Height, weight, age)

Qualitative (categorical)

Data that consists of categories or groups and therefore has no measurement meaning. (ex. Location, color, grade)

Discrete

Data whose possibilities are finite or infinitely countable (if you can count it, ex. integers)

Continuous

Data whose possibilities are infinite on a continuous scale (if you can measure it, ex. Between 0 and 1)

Nominal data

Data that consists only of names or categories, no group is better nor worse (ex. Red, blue, and yellow)

Ordinal data

Data that consists of categories that can be ordered, but differences cant be found or are meaningless (ex. Shirt sizes)

Interval measurement

Data where the differences are meaningful but there is no natural starting point (ex. Temperature in fahrenheit)

Ratio measurement

Data where the differences are meaningful but there is a natural starting point (ex. Kelvin, price)

Bias

When a sample is not representative of the entire population

Voluntary response data

Responders decide whether or not to be included, bias because only extremes respond (ex. Online polls)

Reported results

Data that is provided by the subject, rather than the researcher actually measuring and gathering the data themselves

Observational study

Observe and measure characteristics but don't attempt to modify the subjects being tested, cannot determine cause and effect

Experiment

We apply some treatment and then proceed to observe the effects on subjects. Allows you to create a cause and effect relationship (only way to find this relationship)

Simple random sample (SRS)

Members from the population are selected so that every sample of the same size has the same chance of being chosen

Cluster sample

Divide the population into homogenous groups (strata). Randomly select some of the strata. Then study all members of the selected strata.

Random sample

contains members from a population where every individual member has an equal chance of being selected

Systematic sample

randomly select a starting point and take every nth individual

Convenience sample

Sampling a group that is readily available to reach

Stratified sample (block design)

Divide population into homogeneous groups (strata) and then take simple random samples of each strata

Subjects (experimental units)

entities to be studied (ex. Individual people or entire group)

Observations

What the researcher is measuring or observing on the subjects

Treatment

What is being done to the subjects in an experiment

Replication

Process of repeating the treatments and observations several times in order to collect data (sample size)

Placebo

Treatment that has no active properties (ex. Sugar pill)

Placebo effect

When a person's mental or physical health appears to improve after taking a placebo

Blind experiment

When subjects don't know they are being studies or what treatment they are getting

Double blind experiment

Both the researchers nor the subjects don't know who is getting which treatment

Confounding variables

Other variables that affect the outcome of an experiment. Variables that the researcher may know about and attempt to control in a study.

Lurking variable

Variables that affect the outcome of an experiment and are generally not known by the researcher when the study is created

Matched pair

members of two samples are dependent/related in some way (ex. Pre + post test)

Components necessary to fully describe a study design

1. Type of design: observational or experimental, 2. Type of sampling to be used (SRS, stratified, etc), 3. Complete description of how you will select subjects, how many, and how you will ensure randomness, 4. Describe what statistics you will gather and how you will compare them