Duplicate of AP Statistics Vocabulary

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

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

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

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

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

A variable of description rather than value.

Making conclusions about a larger population based on sample data.

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

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

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

The variable that may predict the changes in another variable.

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

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

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

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

A graph of quantitative data with two clear peaks.

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

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

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

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

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

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

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

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

A graph of the five-number summary.

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

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

The most common number.

An event that rarely would have happened just by chance.

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

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

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

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

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

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

An individual that falls well outside the pattern of data.

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

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

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

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

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

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

A variable that takes on an infinite number of values.

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

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

The probability that an event does NOT happen.

The maximum minus the minimum.

The occurrence of two events at the same time.

The range from Q1 to Q3.

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

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

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

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

The exact counts of a variable.

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

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

A study where researchers deliberately impose treatment on the individuals.

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

A number that describes some characteristic of a population.

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

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

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

The distribution of just one sample.

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

A model that describes the overall pattern of a distribution.

The standard deviation of a significance test or confidence interval.

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

The distribution of many samples.

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

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

The middle number (resistant to outliers).

The set of all possible outcomes of a chance process.

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

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

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

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

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

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

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

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

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

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

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

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

A sample selected by taking individuals that select themselves.

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

A variable of value rather than description.

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

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

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

A non-symmetric distribution that has a distinct tail.

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

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

The science of data.

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

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

Decisions based through a process of chance.

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