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39 Cards in this Set
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random

an outcome is random if we know the possible values it can have, but not which particular value it will be, all values are equally possible outcomes


simulation

models a real world situation by using randomdigit outcomes to mimic the uncertainty of a response variable of interest


response variable

values of the response variable record the results of each trial with respect to what we were interested in


sample survey

a study that asks questions of a sample drawn from some population in the hope of learning something about the entire population, ex. election polling


bias

any failure in representing the whole population is bias, common errors include: voluntary, nonresponse, response bias, undercoverage


randomization

best defense against bias, each individual is given a fair, random change of selection


census

a sample that consists of the entire population


simple random sample (SRS)

made of randomly selected individuals. Each individual in the population has the same probability of being in the sample. All possible samples of size n have the same chance of being drawn.


stratified random sample

a sampling design in which the population is divided into several subpopulations, or strata, and random samples are drawn from each stratum


voluntary response bias

when individuals choose to participate, it is immediate bias


nonresponse bias

when a large fraction of those sampled fails to respond, those who do respond are likely to not represent the entire population


undercoverage

parts of population are not represented


response bias

Fancy term for lying when you think you should not tell the truth, or forgetting. This is particularly important when the questions are very personal (e.g., “How much do you drink?”) or related to the past.


question wording

to prevent bias, wording in survey questions must be neutral and clear


observational study

a study based on data in which no manipulation of factors has occurred


experimental study

a study wherein factors are manipulated to create treatments, subjects are randomly assigned to treatments and the responses are compared across treatment levels


retrospective study

looking back at outcomes


prospective study

subjects are followed to observe future outcomes


factor

a variable whose levels are controlled by the experimenters, experiments aim to discover the effects that the differences in factor levels may have on the responses of experimental units


experimental units

individuals on whom an experiment is performed, called subjects or participants when human


levels/treatment of a factor

the different levels of a single factor that is controlled


control group

assigned to a baseline treatment level, a default treatment which is well known, or a null, placebo treatment, their responses provide a base for comparison


single and double blinding

two groups of individuals in an experiment: those who could influence the results (subjects, technicians) and those who could influence the results (judges, physicians, etc.) if one group is blinded (single), if both (double)


placebo

a treatment with no known effect, given so all subjects experience the same conditions, can cause placebo effect


blocking/stratified

when groups of experimental units are similar, it can be a good idea to group them, this can clarify the responses better


confounding

when the levels of one factor are associated with the levels of another factor in a way that their effects cannot be separated, the two factors are confounding


independent

events are independent if knowing that one event occurs does not change the probability that the other occurs


probability

the likelihood of an event occurring, a number between 0 and 1, to calculate: for A and B to occur, p(A) x p(B), for either A or B to occur, p(A) + p(B)


tree diagram

shows sequences of events in branches


sampling distribution of a statistic

tend to be normal distribution about the normal model, follow the 689597.7 rule


central limit theorem

most samples are located around 50% mark, and hover around the true value


sampling error/variability

the amount of variability expected to occur from one sample to another


confidence interval

the extent of the interval on either side of the statistic value is the margin of error, the confidence interval is the estimate +/ margin of error


95% confidence

95% of samples of equal size will produce confidence intervals that capture the true proportion


p

true proportion


p̂

samplebased estimate of the proportion


q

1p


q^

1p̂


parameter vs. statistic

a Parameter is a number describing a characteristic of a Population whereas a Statistic is a characteristic of a Sample
