Stats Unit A

statistics

the science of collecting, organizing, analyzing, and interpreting data to make decisions.

population

the collection of all outcomes, responses, measurements, or counts that are of interest.

• population is the whole thing.

census

the collection of data from every member of a population.

sample

a SUBSET of the population.

parameter

POPULATION

statistic

SAMPLE/ SUBSET

descriptive statistics

involves the organization, summarization, and display of data

• ex: tables, charts, averages

inferential statistics

involves using sample data to draw conclusions about a population.

nominal level of measurement

• QUALITATIVE DATA

• categorized using names, labels, or qualities

• NO MATHEMATICAL computations can be made

• categories only

ordinal level of measurement

• QUALITATIVE AND QUANTITATIVE DATA

• data can be arranged in order, or ranked

• differences between data entries is not meaningful

• “order” “rank” are clue words.

interval levels of measurement

• QUANTITATIVE DATA

• data can be ordered

• differences between data entries is meaningful

• a zero does not imply “none”

• ex: temperature, 0 degrees does not mean no temp

ratio level of measurement

• QUANTITATIVE DATA

• zero means zero

• a ratio of two data values can be formed

• one data value can be expressed as a multiple of another. (able to divide or multiple means it is meaningful.)

observational studies

a researcher observes and measures characteristics of interest of part of a population.

experiment

when a treatment is applied to part of a population

placebo

harmless fake treatment that is made to look like the real treatment

simulation

uses a mathematical or physical model to reproduce the conditions of a situation or process

• often involes computers

survey

asking questions

placebo effect

when a subject reacts favorably to a placebo when they were actually given the fake treatment

blinding

in which the subjects do not know whether they are recieving the treatment or a placebo

double blind

neither the subject nor the experimenter knows if the subject is receiving a treatment or a placebo.

completely randomized design

subjects are assigned to different treatment groups through random selection

randomized block design

divide subjects with similar characteristics into blocks, and then within each block, randomly assign subjects to treatment groups

why is it important to have a big sample size?

more subjects, more valid results

random sample

every member of the population has an equal chance of being selected.

simple random sample

every possible sample of the same size has the same chance of being selected

stratified sample

• deals with groups

• divide a population into groups and select a random sample from each group

cluster sample

divide the population into groups and select all of the members in one or more, but not all, of the clusters.

For example:

• in a county, you could divide the households into clusters according to zip codes, then select all the households in one or more, but not all, zip codes.

systematic sampling

• PATTERN

• assigning every “100th” person

convenience sample

choose only members of the population that are easy to get.

• NOT RECOMMENDED

statistical significance

if the percentage is high. it has to be an extreme outcome

• Numbers

practical significance

• meaningful

• it is possible that some treatment or finding is effective, but common sense might suggest that the treatment or finding does not make enough of a difference to justify its use or to be practical.