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Statistics Portage CNSL 503 Exam 3 Questions and Answers

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Statistics Portage CNSL 503 Exam 3 Questions and Answers alpha level - ANSWERS-the maximum probability that one would make a type I error if the null hypothesis is true; a decision criterion set prior to data collection p value - ANSWERS-the probability that we have made a type I error if the null hypothesis is true; based on the collected data problems with hypothesis testing - ANSWERS-1. conventional levels of alpha (0.05, 0.01 or 0.001) are arbitrary 2. a larger sample size is more likely to achieve statistical significance than smaller sample size (statistical significance does not equal meaningful significance) 3. there is too much reliance on the p value as the sole measure upon which conclusions are made (variances) 4. people (including researchers) misinterpret the p value and the results of hypothesis testing 5. there is a bias towards publishing statistically significant results over non-significant results; this leads to an incomplete and biased picture of the findings How to combat problems with hypothesis testing? - ANSWERS-supplement hypothesis testing with additional measures, such as effect size and confidence levels parameter - ANSWERS-a numerical value that describes a population example of parameter - ANSWERS-population mean statistic - ANSWERS-a value that describes a sample; used to estimate population parameter

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Statistics Portage CNSL 503
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Statistics Portage CNSL 503

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Subido en
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Statistics Portage CNSL 503 Exam 3
Questions and Answers10

alpha level - ANSWERS-the maximum probability that one would make a type I error if the null
hypothesis is true; a decision criterion set prior to data collection



p value - ANSWERS-the probability that we have made a type I error if the null hypothesis is
true; based on the collected data



problems with hypothesis testing - ANSWERS-1. conventional levels of alpha (0.05, 0.01 or
0.001) are arbitrary

2. a larger sample size is more likely to achieve statistical significance than smaller sample size
(statistical significance does not equal meaningful significance)

3. there is too much reliance on the p value as the sole measure upon which conclusions are
made (variances)

4. people (including researchers) misinterpret the p value and the results of hypothesis testing

5. there is a bias towards publishing statistically significant results over non-significant results;
this leads to an incomplete and biased picture of the findings



How to combat problems with hypothesis testing? - ANSWERS-supplement hypothesis testing
with additional measures, such as effect size and confidence levels



parameter - ANSWERS-a numerical value that describes a population



example of parameter - ANSWERS-population mean



statistic - ANSWERS-a value that describes a sample; used to estimate population parameter

, example of a statistics - ANSWERS-sample mean



In order to make correct inferences about the population, the sample must... - ANSWERS-reflect
the population



representative sample - ANSWERS-consist of members that possess the same characteristics as
those of the population



biased sample - ANSWERS-a sample that is not representative of the population



5 types of sampling techniques - ANSWERS-simple random sampling, systematic sampling,
stratified sampling, cluster sampling, and convenience sampling



simple random sampling - ANSWERS-each member of the population has an equal chance of
being selected for the sample



example of simple random sampling - ANSWERS-everyone in the population of interest rolls a
die, everyone rolling a 6 gets selected



In simple random sampling, the probability that each member is selected is ____ of one
another. - ANSWERS-independent



Simple random sampling is ideal for ____ populations. - ANSWERS-small



systemic sampling - ANSWERS-random sampling with a system in which a starting point is
random and each subsequent member is selected based on a fixed interval
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