# Reporting Period Numeric Mn.docx

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### Reporting Period Numeric Mn.docx

• 2. significant relationship between …? For example:Is there a significant relationship between the age of managers? and their attitudes towards the reorganization?A research hypothesis is a testable statement of opinion. It is created from the research question by replacing the words “Is there” with the words “There is,” and also replacing the question mark with a period. The hypotheses for the two sample research questions would be:There is a significant relationship between the age of managers and their attitudes towards the reorganization. It is not possible to test a hypothesis directly. Instead, you must turn the hypothesis into a null hypothesis. The null hypothesis is created from the hypothesis by adding the words “no” or “not” to the statement. For example, the null hypotheses for the two examples would be:There is no significant relationship between the age of managersand their attitudes towards the reorganization.There is no significant difference between white and minority residentswith respect to what they feel are the most important problems facing the community.All statistical testing is done on the null hypothesis…never the hypothesis. The result of a statistical test will enable you to either:1) reject the null hypothesis, or2) fail to reject the null hypothesis. Never use the words “accept the null hypothesis.” *Source: StatPac for Windows Tutorial. (2017). User’s Guide; Formulating Hypotheses from Research Questions. Retrieved May 17, 2019, from https://statpac.com/manual/index.htm?turl=formulatinghypothesesfromresearchquestion s.htmWhat does significance really mean?“Significance is a statistical term that tells how sure you are that a difference or relationship exists. To say that a significant difference or relationship exists only tells half the story. We might be very sure that a relationship exists, but is it a strong, moderate, or weak relationship? After finding a significant relationship, it is important to evaluate its strength. Significant relationships can be strong or weak. Significant differences can be large or small. It just depends on your sample size.To determine whether the observed difference is statistically significant, we look at two outputs of our statistical test:P-value: The primary output of statistical tests is the p-value (probability value). It indicates the probability of observing the difference if no difference exists.The p-value from the above example, 0.9926, indicates that we DO NOT expect to see a meaningless (random) difference of 5% or more in ‘hospital beds’ only about 993 times in 1000 there is no difference (0.9926*1000=992.6 ~ 993).Note: This is an example from the week1 exercise.The p-value from the above example, 0.0001, indicates that we’d expect to see a meaningless (random) “number of the employees on payer” difference of 5% or more only about 0.1 times in 1000 (0.0001 * 1000=0.1). CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-40.82 ; 40.44]):CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-382.16 ; 125.53]):The boundaries of this confidence interval around the difference also provide a way to see the upper [40.44] and lower bounds [-40.82].As a summary:“Statistically significant means a result is unlikely due to chance.The p-value is the probability of obtaining the difference we saw from a sample (or a larger one) if there really isn’t a difference for all users.Statistical significance doesn’t mean
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