Duplicate of AP Statistics Vocabulary

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

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

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

A graph of quantitative data with two clear peaks.

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

An individual that falls well outside the pattern of data.

The exact counts of a variable.

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

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

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

A variable of description rather than value.

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.

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

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

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

The middle number (resistant to outliers).

Making conclusions about a larger population based on sample data.

A variable that takes on an infinite number of values.

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

A sample selected by taking individuals that select themselves.

__% 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.

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

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

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

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

The most common number.

An event that rarely would have happened just by chance.

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

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

A study where researchers deliberately impose treatment on the individuals.

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

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

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

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

The standard deviation of a significance test or confidence interval.

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

The distribution of just one sample.

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

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

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

A variable of value rather than description.

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

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

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

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

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

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

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

The variable that may predict the changes in another variable.

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

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

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

The set of all possible outcomes of a chance process.

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

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

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

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

A number that describes some characteristic of a population.

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

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

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

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

A model that describes the overall pattern of a distribution.

Decisions based through a process of chance.

The distribution of many samples.

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

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

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

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

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

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

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

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

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

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

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

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

The science of data.

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

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

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

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

The probability that an event does NOT happen.

The range from Q1 to Q3.

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

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

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

The maximum minus the minimum.

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

A graph of the five-number summary.

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

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

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

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

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

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

A non-symmetric distribution that has a distinct tail.

The occurrence of two events at the same time.