CONFIDENCE IS (the) KEY ;)

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Across
  1. 1. When checking for independence the sampling size should be no more than this percent of the population
  2. 5. The probability that the confidence interval captures the population parameter is either one or
  3. 7. To calculate a confidence interval the sampling distribution must be
  4. 10. You must check these before calculating a confidence interval
  5. 11. Which sided hypothesis states that the parameter is different from the null hypothesis value
  6. 13. You can memorize this kind of score for a confidence interval
  7. 15. What kind of sample should be used to calculate a confidence interval
  8. 17. This hypothesis can incorporate an inequality
  9. 19. This is another name for a claim
  10. 20. When the P value is less than this percent it is considered statistically significant
Down
  1. 2. You keep this hypothesis only until proven otherwise
  2. 3. A confidence interval captures the actual value of this
  3. 4. Always interpret the results of a problem in
  4. 6. We specify a particular t distribution by giving its degrees of
  5. 8. A significance test does not shed as much light as a
  6. 9. Means that the stated confidence level is still pretty accurate
  7. 12. Confidence intervals are statements about
  8. 14. This sized P-value gives the strongest evidence against the null hypothesis
  9. 16. As the margin of error of a confidence interval gets smaller this increases
  10. 18. This statistic tells how far away the sample result is from the null parameter value