WGU= C431: Healthcare Research and Statistics Topic 2- Statistical Method exam with 100% correct answers already graded A+

MEAN average MEDIAN Middle number standard deviation · The average dispersion from the mean- how far apart are the data points from the mean. How spread out your data is within the curve. The lowest it can be is zero. Low standard deviation (More reliable) is data sets are not spread out. High is they are (less reliable). o 99.7% of the data are within 3 standard deviations of the mean. P+ 3a or P-3a o 95% within 2 standard deviations P+2a or P- 2a o 68% within 1 standard deviations p+a or P-a o Mean (average) = P standard deviation curve Negative skewed- curve trends to the right Positive skewed- curve trends to the left Normal- mean and median will have the same value- equally distributed P value Probability value that we would have seen our data (or something more unexpected) just by chance if the null hypothesis (null value) is true. The probability of achieving your results by chance. · Small p-values mean the null value is unlikely given our data · The p-value that is less than alpha is what we expected. Making the null hypothesis not true- therefore we are rejecting the hypothesis · If the p value is higher than the alpha, we are failing to reject the hypothesis. Type 1 error - is what happens when we reject a true null hypothesis- we rejected it and we should have not rejected it. Also known as a false positive Five parts of a significance test Assumptions hypothesis Test statistics p Value conclusion Hypothesis A testable prediction, often implied by a theory Null hypothesis a statement that parameter(s) take special value(s)- usually no effect/ no relationship. INNOCENT Alternative hypothesis states that parameter value falls in some alternative range of values/ a relationship. GUILTY test statistic Compares data to what null hypothesis predicts, often by finding the number of standard errors between sample point estimate and null value of parameter. ALLOWS FOR A COMPARISON P Value the smaller the p-value, the stronger the evidence against null hypothesis. Effect size the influence that the size of your sample can have on your study. A P-value of a .08 is more evidence against the null hypothesis than a p-value of .04 False- a small pvalue means the value of the statistic we observed in the sample is unlikely to have occurred. The smaller the pvalue, the stronger the evidence against the null hypothesis If two independent studies are done on the same population with the purpose of testing the same hypothesis, the study with the large sample size is more likely to have a smaller p-value than the study with the smaller size. · When the null hypothesis is true, a small pvalue should be equally likely regardless of sample size. · When the null hypothesis is false, hypothesis test done with large sample sizes are more likely to reveal the false null, and hence more likely to result in a small p-value. A researcher who tried to learn statistics without taking a formal course does a hypothesis test and gets a pvalue of .024. He says, " there is a 98.6% change that the alternative hypothesis is false, so the null hypothesis is true. " What, if anything is wrong with his statement? false, so the null hypothesis is true. " What, if anything is wrong with his statement? · The researcher is claiming that 1-pvalue is the probability that the alternative hypothesis is false. The pvalue is not a probability of an alternative (or null) hypothesis being true or false If you get a pvalue of .13, it means there is a 13% chance that the sample average equals the population average. false Confidence intervals (CI) % how confident you are What determines the width of the CI? · Sample size- the width reduces as the sample size increases