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39 Cards in this Set

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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
models a real world situation by using random-digit 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
any failure in representing the whole population is bias, common errors include: voluntary, nonresponse, response bias, undercoverage
best defense against bias, each individual is given a fair, random change of selection
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
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
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)
a treatment with no known effect, given so all subjects experience the same conditions, can cause placebo effect
when groups of experimental units are similar, it can be a good idea to group them, this can clarify the responses better
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
events are independent if knowing that one event occurs does not change the probability that the other occurs
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 68-95-97.7 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
true proportion
sample-based estimate of the proportion
parameter vs. statistic
a Parameter is a number describing a characteristic of a Population whereas a Statistic is a characteristic of a Sample