AP Statistics Vocabulary

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

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

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

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

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

The science of data.

The range from Q1 to Q3.

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

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

The middle number (resistant to outliers).

A number that describes some characteristic of a population.

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

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

The set of all possible outcomes of a chance process.

The occurrence of two events at the same time.

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

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

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

The standard deviation of a significance test or confidence interval.

A graph of quantitative data with two clear peaks.

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

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

The distribution of just one sample.

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

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

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

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

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

The probability that an event does NOT happen.

The exact counts of a variable.

The distribution of many samples.

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

A variable that takes on an infinite number of values.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The maximum minus the minimum.

A sample selected by taking individuals that select themselves.

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.

Explains how well the regression line fits the data, from 0 (weak) to 1 (strong).

The most common number.

A graph of the five-number summary.

The variable that may predict the changes in another variable.

Decisions based through a process of chance.

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

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

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

A sample by putting individuals into population-like groups and randomly choosing one group.

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

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.

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

A model that describes the overall pattern of a distribution.

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

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

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

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

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

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

Making conclusions about a larger population based on sample data.

A non-symmetric distribution that has a distinct tail.

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

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

A variable of description rather than value.

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

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

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

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

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

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

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

An individual that falls well outside the pattern of data.

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

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

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

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

An event that rarely would have happened just by chance.

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

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

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

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

A variable of value rather than description.

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