Term
|
Definition
|
|
Term
|
Definition
| A test used to evaluate how closely observed results fit expectations. |
|
|
Term
| procedure for doing a chi-square test |
|
Definition
| 1: State a null hypothesis (H0).
2: Calculate your test statistic (X2).
3: Degrees of freedom (df).
4: Statistical significance (p). |
|
|
Term
|
Definition
states that the observed results will be the same as the expected results
needs to be tested |
|
|
Term
| how to calculate the test statistic (X2) |
|
Definition
|
|
Term
|
Definition
|
|
Term
| Statistical significance (p). |
|
Definition
| the probability, p of obtaining the observed results if the null hypothesis is true |
|
|
Term
| A generally accepted p for acceptance or rejection of a hypothesis |
|
Definition
|
|
Term
| alternative hypothesis (HA) |
|
Definition
| hypothesis that states that there is a difference between observed and expected |
|
|
Term
| when to accept the null hypothesis |
|
Definition
| when X2 is within the range on the table for the degree of freedom |
|
|
Term
| when to reject the null hypothesis |
|
Definition
| when X2 is not within the range on the table for the degree of freedom |
|
|
Term
|
Definition
| basically a table that shows relationship between two events |
|
|
Term
| The Null Hypothesis for a contingency table |
|
Definition
| to test whether the frequencies of observations in the rows are independent of the frequencies of the observations in the columns. |
|
|
Term
| example of a contingency table |
|
Definition
|
|
Term
| how to calculate expected numbers in a contingency table |
|
Definition
here's an example:
(# of males / total population) * (# of specific variable) = expected number for that variable
(# of females / total population) * (# of specific variable) = expected number for that variable |
|
|
Term
| how to calculate d.f. in a contingency table |
|
Definition
| (# of R's - 1) * (# of C's - 1) = d.f. |
|
|