Across
- 2. Things that must be checked before performing a test. Examples include Random, Large Counts, and Independence
- 7. An error in which you reject the null hypothesis when it is actually true
- 9. A graph in which there are no outliers or skewness
- 11. The probability that the test rejects the null hypothesis
- 12. The percentage of confidence we use to run the test. Can be any percentage, but most common is 95% and 99%
- 14. a one sample test about a population mean
- 15. The z-score or t-score. Used to find the P-value with Inverse Cdf
- 16. The step in which the test is carried out. The Critical value is found, and well as the P-value. A normally distributed graph is drawn to show this
- 17. The level of significance. It is the probability of making a Type I error
- 19. The probability that the statistic will be as extreme or more extreme than what is given from the sample
Down
- 1. A range of values in which we are confident the parameter is contained in
- 3. The step in which you list all given values, and what "P" represents
- 4. The contradictory statement to the Null Hypothesis
- 5. A value taken from a population
- 6. The step that must be taken before completing a test. It includes Random, Large Counts, and Independence
- 8. The percentage, decimal, or fractional value of what is being tested
- 10. An error in which you fail to reject the null when you should have rejected it
- 13. The given value/assumption that is equal to the given proportion
- 15. The final step of a test Where the P-value is stated, the alpha level is stated, and whether or not we reject or fail to reject the null
- 18. a one sample test about a population proportion