AP Statistics Vocabulary Cumulative Review

The sum of the values of the observations divided by the number of observations.

A variable that takes on an infinite number of values.

When only either the researchers or the subjects know which treatments were assigned to groups.

The occurrence of one event or the other, or both.

A sampling distribution of at least 30 will be approx normal even if the population is not.

A sample where each individual was randomly chosen from the population.

The occurrence of two events at the same time.

A fixed value used as a cutoff on what probability is or is not statistically significant.

A non-symmetric distribution that has a distinct tail.

When the design of a study would consistently give an over or underestimate of the true value.

The middle number (resistant to outliers).

Occurs when we fail to reject the Null hypothesis when it was actually false.

The probability that the statistic would have taken on a value given the Null hypothesis true.

The distribution of a random variable of continuing trials until a certain number of successes.

A Normal distribution with mean 0 and standard deviation of 1.

A graph that shows the relationship between two quantitative variables using dots.

An outcome of a chance process is more likely to occur if it has not occurred for a long time.

When a distribution is even across the full range of values of the variable.

The claim that we do not believe to be true that we are trying to find evidence against.

Explains the linear relationship between two quantitative variables, from -1 to 1.

A range of plausible values for a parameter, based on a statistic.

Individuals are monitored for measures of variables of interest but not influenced in response.

A smaller group of individuals whose information is used to infer about a larger group.

A graph of quantitative data with two clear peaks.

A group of experimental units that are similar and so are treated as one experimental unit.

A number between 0 and 1 that describes the proportion of an outcome in a chance process.

A sample obtained by putting individuals into like-groups and randomly choosing some from each.

The amount of counts of a variable out of all counts, often expressed as a percent.

When knowing the value of one variable helps to predict the value of another.

The maximum minus the minimum.

The proportion of observations that are lower than a particular observation.

The four evenly sized sections of a set of ordered data.

The number of individuals that would be found in each category if the Null hypothesis is true.

The standard deviation of a significance test or confidence interval.

The probability that a test will reject the Null when the Alternative is true.

A variable of value rather than description.

The number of standard deviations to be added/subtracted from the test statistic (z* or t*)

The response value a Least Squares Regression Line would expect for a given explanatory value.

A number that describes some characteristic of a population.

A sample where each individual was randomly chosen and randomly assigned to a treatment.

An inactive (fake) treatment for which some subjects may actually respond.

When the effects of two different variables on a response variable cannot be told apart.

A graph of the five-number summary.

A sample selected by taking individuals that select themselves.

The # of standard deviations an observation falls from the center of a Normal distribution.

A model that describes the overall pattern of a distribution.

The entire group of individuals which we are interested in knowing something about.

The science of data.

The total value to be added/subtracted from the test statistic to find the interval max/min.

The range from Q1 to Q3.

The probability that an event does NOT happen.

An imitation of a chance behavior that uses a model that accurately represents the situation.

The exact counts of a variable.

A sample selected by taking individuals that were easy to reach.

In experiments, when all variables except for the explanatory variable must be kept the same.

A variable that takes on a finite number of values (usually no decimals).

The variable that may predict the changes in another variable.

The probability that an event will occur given that another event has already occurred.

Minumum, Q1, Median, Q3, and Maximum values of a data set.

An event that rarely would have happened just by chance.

A study that attempts to survey every individual of the population.

An accurate statistic used to approximate the parameter of a population.

A procedure using a sample statistic to make a decision about the validity of a claim.

The set of all possible outcomes of a chance process.

The distribution of a random variable with the number of trials set in advance.

The proportion of a chance process will approach a single value as you do more trials.

Occurs when we reject the Null hypothesis when it was actually true.

A variable of description rather than value.

A common form of blocking in comparing two different treatments, often on the same sample.

The distribution of many samples.

A family of distributions that always take on positive values and are skewed right.

The fraction of the variation that can be explained by the least-squares regression line (r²).

When it is not possible for two event to occur simultaneously.

A class of density curves that are symmetric, single-peaked, and bell-shaped.

__% of all such samples would produce a range of values that would capture the true value.

A number from a sample used to estimate some characteristic of a population.

An individual that falls well outside the pattern of data.

When randomly selected individuals in a sample do not provide accurate information.

The typical or common distance observations are away from the mean.

A study where researchers deliberately impose treatment on the individuals.

The claim that we actually believe to be true that we are trying to find evidence for.

The number of individuals of a sample found in each category in response to a survey.

The smallest collection of individuals to which a treatment is to be applied.

A diagram used to display the sample space of a chance process involving a sequence of outcomes

The individual terms that are added together to form the test statistic for categorical data.

When randomly selected individuals cannot be contacted or refuse to participate.

The (inaccurate) use of a regression line to estimate outside the range of the given data.

The distribution of just one sample.

When neither the researchers nor the subjects know which treatments were assigned to groups.

Explains how well the regression line fits the data, from 0 percent to 100 percent.

A family of distributions like the Normal curve expect with more variability in the samples.

A graph that shows bars that have ranges for counts of a continuous variable.

Making conclusions about a larger population based on sample data.

The distribution of total values for categorical variables on a one-way or two-way table.

The difference between an observed value and its predicted/expected value.

Put individuals into population-like groups and randomly choosing one or more of those groups.

When the right and left sides of a distribution are mirror images of each other.

When a group of the population does not get accurate representation in a sample.

The most common number.

Decisions based through a process of chance.