|population||is the entire group of objects you want to study.
ex: scores, people, measurements
|sample||is a smaller subset chosen from the population and a representative of the population.|
|parameter||is a number which describes a property of only a sample.|
|random sample||every object in the population is equally likely to be picked for the sample.
ex: pick name out of hats.
|systematic sample||every Kth object is chosen for the sample.
ex: think assembly line pick every 10th computer off the line.
|convenience/volunteer/self-selected||are non scientific approaches that will not lead to a representative sample.
ex: online surveys, phone polling, restaurant surveys
|cluster sample||is the method that picks groups randomly from the population instead of pick one object.
Every object in randomly selected groups forms our sample.
|stratified sample||is the method when we divide the entire population into meaningful groups.
Ex: republican and democrats, male or female
randomly sample to fill each group
|1st: randomly pick groups from population.
2nd: sample is every object from the groups
|1st: subdivide population with named groups.
2nd: randomly select objects from each group.
|quantitative data||is numeric data in which you can count.
ex: ages, weight
refers to data type not a level of measurement
|categorical (qualitative) data||is NOT numeric but instead you break them into categories by labels.
ex: eye color, letter grades
NOT how many people.
|ratio level||means 0=None
Can not have negative numbers
ex: age, length, weight, measurement of amounts
|interval level||0 not equal to NONE. can be negative.
|ordinal level||categories have a built in order. reordering would be confusing.
ex: letter grades a,b,c,d
smallest to largest
|nominal level||categories can be put in any order and not be confusing. can not be arranged in an ordering scheme.
ex: eye color, names, labels, categories.
|discrete data||data you can count. "number of"|
|continuous data||data you can measure
ex: height, length, age