The sampling distribution model of the sample mean or proportion from a random sample is approximately Normal for large n, regardless of the distribution of the population, as long as the observations are independent

Estimating the standard deviation of a sampling distribution using statistics found from the data

In a confidence interval, the extent of the interval on either side of the observed statistic value - found by multiplying a critical value of the sampling distribution and the standard error.

Based upon the hypothesis that the proportion is the same in two different groups, we estimate the common proportion by combining the data from our two samples

The difference between the null hypothesis value and the true value of a model parameter

Rejecting a null hypothesis when in fact it is true

When the P-value falls below the alpha level

The probability that a hypothesis test will correctly reject a false null hypothesis (1- $$\beta$$ )

The difference we expect to see from one random sample to another

If assumptions are met, the sampling distribution is modeled by a Normal model with a mean equal to the population mean, $$\mu$$ , and a standard deviation equal to $$\frac{\sigma}{\sqrt[]{n}}$$

The number of standard errors to move away from the sample statistic to specify an interval that corresponds to the specified level of confidence.

Computed by taking an Estimate $$\pm$$ Margin of Error, giving parameters for which a specified percentage of samples will yield ranges that capture the true parameter value.

The assertion of "no change from the traditional value," "no effect, "no difference," or "no relationship."

The conditional probability of observing a value for a test statistic at least as far from the hypothesized value as the statistic value actually observed if the null hypothesis is true

Different random samples give different values for a statistic. This model shows the behavior of the statistic over all possible samples for the same size n.

The threshold P-value that determines when we reject a null hypothesis.

Failing to reject a null hypothesis when in fact it is false

Collecting data in such a way to insure that subjects in each sample are not affected by one another

An hypothesis where we are interested in deviations in either direction away from the hypothesized parameter value