unit 2 exam

A table that shows how individuals are distributed along each variable.

The row total or column total in a contingency table.

The distribution of one variable for cases that satisfy a condition on another variable.

The entire group of individuals or instances about whom we hope to learn.

A representative subset of a population, examined in the hope of learning about the population.

A study that asks questions of a sample drawn from some population in the hope of learning something about the entire population.

The best defense against bias, where each individual is given a fair, random chance of selection.

A sample that consists of the entire population.

A numerically valued attribute of a model for a population.

Values that are calculated for sample data.

A list of individuals from whom the sample is drawn.

A sample in which each set of elements in the population has an equal chance of selection.

A sampling design in which the population is divided into several subpopulations and random samples are drawn from each stratum.

Entire groups, or clusters, are chosen at random.

Sampling schemes that combine several sampling methods.

A sample drawn by selecting individuals systematically from a sampling frame.

Bias introduced to a sample when individuals can choose whether to participate.

Biases the sample by giving a part of the population less representation.

Bias introduced when a large fraction of those sampled fails to respond.

Anything in a survey design that influences responses.

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

An observational study in which subjects are selected and their previous conditions or behaviors are determined.

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

Participants who are similar in ways not under study may be matched and then compared on the variables of interest.

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

A variable whose levels are manipulated by the experimenter.

A variable whose values are compared across different treatments.

Specific values that the experimenter chooses for a factor.

Process, intervention, or other controlled circumstance applied to randomly assigned experimental units.

When groups of experimental units are similar in a way that is not a factor under study, it is often a good idea to gather them together into blocks and then randomize the assignment of treatments within each block.

An experiment must assign experimental units to treatment groups using some form of randomization.

Control aspects of the experiment that may have an effect on the response, but are not the factors being studied.

Replicate over as many subjects as possible.

When an observed difference is too large to have occurred naturally, it is considered statistically significant.

All experimental units have an equal chance of receiving any treatment.

Participants are randomly assigned to treatments within each block.

Participants are paired with similar subjects, and the difference in response variables is compared.

Experimental units assigned to a baseline treatment level.

Experimental units assigned to a baseline treatment level, providing a basis for comparison.

Any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups.

When either those who could influence the results or those who evaluate the results are blinded.

When both those who could influence the results and those who evaluate the results are blinded.

A treatment known to have no effect.

The tendency of human subjects to show a response even when administered a placebo.

When the levels of one factor are associated with the levels of another factor in such a way that their effects cannot be separated.

A variable associated with both y and x that makes it appear that x may be causing y.

A phenomenon is random if we know what outcomes could happen, but not which particular values will happen.

A single attempt or realization of a random phenomenon.

The value measured, observed, or reported for an individual instance of a trial.

A collection of outcomes.

The collection of all possible outcome values.

The long-run relative frequency of an event's occurrence gets closer to the true relative frequency as the number of trials increases.

Two events are independent if learning that one event occurs does not change the probability that the other event occurs.

A number between 0 and 1 that reports the likelihood of an event's occurrence.

The probability that comes from the long-run relative frequency of an event's occurrence.

The probability that comes from a model, such as equally likely outcomes.

The probability that is subjective and represents personal belief.

An assignment of probabilities to outcomes is legitimate if each probability is between 0 and 1, and the sum of the probabilities is 1.

The probability of the sample space must be 1.

The probability of an event not occurring is 1 minus the probability that it occurs.

The probability that one or the other of two disjoint events occurs is the sum of their individual probabilities.

The probability that both of two independent events occur is the product of their individual probabilities.

The probability of the union of any two events is the sum of their individual probabilities minus the probability of their intersection.

The probability of an event given that another event has occurred.

The probability of the intersection of two events is the product of their individual probabilities.

Events are independent if the probability of one event occurring does not affect the probability of the other event occurring.

A display of conditional events or probabilities that is helpful in thinking through conditioning.

A rule that calculates the conditional probability of an event given another event using the probabilities of the two events and their complements.

A sequence of trials with exactly 2 possible outcomes (success and failure), where the probability of success is constant and the trials are independent. There are a fixed number of trials, n.

A condition for a Binomial Model to be approximately Normal, where there are at least 10 successes and 10 failures, i.e. np ≥ 10 and n(1 - p) ≥ 10.

The probability of event A given event B, which is calculated using Bayes Rule:P(A | B) = P(B | A) P(A) / (P(B | A) P(A) + P(B | Not A) P(Not A)).

The probability of event B, which can be calculated using the Multiplication Rule:P(B and A) = P(B) P(A | B).

A probability model used for binomial distributions, where the probability of exactly k successes in n trials is given by P(X = k) = n! / (k! (n - k)!) p^k (1 - p)^(n - k). The mean is μ = np and the standard deviation is σ = sqrt(np(1 - p)), where n! represents n factorial.