AP STATS: UNIT 3 VOCAB

pseudorandom number

a number generated from a computer or calculator using a program/algorithm

random

an event is random if we know what outcomes could potentially happen, but not exactly which outcomes will happen

response variable

what were measuring in a simulation (or any method of data collection); this is what we record for each trial (based on the give question)

outcome

the result of the most basic event in a simulation

component

the most basic situation/event in a simulation

trial

sequence of components representing events that were pretending will take place

sample survey

method of data collection where we randomly select participants and ask them questions to collect data

simulation

method of data collection that uses random events to model real world scenarios

experiment

method of data collection where people/things are placed into treatment groups and many variables are controlled (only method that allows us to establish cause and effect relationship)

census

sample that consists of the entire population

cluster

sampling method where we separate our population into representative groups, the randomly elect one or more of these groups, and survey every individual in those (selected groups)

response bias

when part of the survey design, and often the wording of a question, influences response

srs

sampling method that is equivalent to pulling names out of a hat; each individual and each sample is equally likely to be chosen

bias

this is when a particular group is over or underrepresented in sample that has been collected

sample

group of individuals who are actually surveyed

voluntary response sample

sampling method where we give people the opportunity to respond, such as an online poll

population

the "whole group" of individuals that we are trying to generalize to

non-response bias

when some/many individuals do not respond and many have a different opinion

population parameter

number we are trying to estimate based on our sample data

stratified

sampling method where we separate our population into homogenous groups (based on some similar characteristic), then we randomly sample some individuals from each group

voluntary response bias

when those with the strongest opinions are more likely to respond and are overrepresented

undercoverage

when part of the population is left out due to our sampling method

sampling variability

the idea that we know statistics will vary from sample to sample

sampling frame

group of all individuals who could have been sampled

multistage

type of sampling where we combine 2 or more sampling methods

systematic sample

when we sample every nth person on a list, starting from a random place on the list

statistic

number that is calculated from and summarizes a sample of data

convenience

sampling method where we sample the people/things that are easiest for us to sample

observational study

study based on data in which no manipulation of factors has been employed

retrospective study

observational study in which subjects are selected and then their previous conditions or behaviors are determined. because retrospective studies are not based on random samples, they usually focus on estimating differences between groups or associations between variables

prospective study

an observational study in which subjects are followed to observe future outcomes.

experiment

manipulates factor levels to create treatments, randomly assigns subjects to these treatment levels, and then compares the responses of the subject groups across treatment levels

random assignment

to be valid, an experiment must assign experimental units to treatment groups at random. this is called random assignment.

factor

variable whose levels are controlled by the experimenter. experiments attempt to discoed the effects that differences in factor levels may have on the responses of the experimental units

response

a variable whose values are compared across different treatments. in a randomized experiment, large response differences can be attributed to the effect of differences in treatment level

experimental units

individuals on whom an experiment is performed. usually called subjects or participants when they are human

level

specific values that the experimenter chooses for a factor are called the levels of the factor

treatment

the process, intervention, or other controlled circumstance applied to randomly assigned experimental units. treatments are the different levels of a single factor or are made up of combinations of levels of two or more factors

principles of experimental designs

control, randomize, replicate, block

statistically significant

when an observed difference is too large for us to believe that it is likely to have occurred naturally, we consider the difference to be statistically significant. Subsequent chapters will show specific calculations and give rues, but the principle remains the same

control group

the dealt treatment which is del understood, jul, placebo treatment. the response provides a basis for comparison

blinding

any individual associated with an experiment who is not aware of how subjects have been allocated to treatments groups is said to be blind

placebo

treatment known to have no effect

placebo effect

the tendency of many human subjects to show a response even when administered a placebo

block

when groups of experimental units are similar, it is good to gather them into blocks.

matching

subjects who are similar i ways not under study may be matched and then compared with each other on the variables of interest. Like blocking, it reduces unwanted variation

designs

in a randomized block design, the randomization occurs only within block. In a completely randomized design, all experimental units have an equal chance of receiving any treatment

confounding

when the levels of one factor are associated with the levels of another factor so their effects cannot be separated