ap stats unit 6 vocab b

Significance test

A formal procedure for using observed data to decide between two competing claims (called hypotheses). The claims are usually statements about population parameters.

Null hypothesis

The claim that we weigh evidence against in a significance test.

H0

Variable for Null Hypothesis

Alternative hypothesis

The claim that we are trying to find evidence for.

Ha

Variable for Alternative Hypothesis

One sided alternative

If the alternative hypothesis states that a parameter is greater than the null value or if it states that the parameter is less than the null value.

Two sided alternative

If the alternative hypothesis states that the parameter is different from the null value (it could be either greater than or less than).

P value

The probability of getting evidence for the alternative hypothesis Ha as strong or stronger than the observed evidence when the null hypothesis H0 is true.

Significance level

The value that we use as a boundary to decide if an observed result is unlikely to happen by chance alone when the null hypothesis is true.

a

Variable for Significance Level

Conditions for Performing a Significance Test about a Proportion (Fill in the blank.)

Random, 10%, Large Counts (p0 now)

Random

The data come from a random sample from the population of interest.

10%

When sampling without replacement, n< 0.10N.

Large Counts

Both np0 and n(1 – p0) are at least 10.

Standardized test statistic

Measures how far a sample statistic is from what we would expect if the null hypothesis H0 were true, in standard deviation units. That is, standardized test statistic = (statistic – parameter)/(standard error of statistic)

One sample z statistic for a proportion (formula)

z = (p^ – p0)/(square root of (p0(1– p^))/n)

One sample z test for a proportion

A significance test of the null hypothesis that a population proportion p is equal to a specified value.

Type I error

Occurs if we reject H0 when H0 is true. That is, the data give convincing evidence that Ha is true when it really isn’t.

Type II error

Occurs if we fail to reject H0 when Ha is true. That is, the data do not give convincing evidence that Ha is true when it really is.

Power

The probability that the test will find convincing evidence for Ha when a specific alternative value of the parameter is true.