A hypothesis test is a statistical test that is used to determine whether there is enough evidence in a sample of data to infer that a certain condition is true for the entire population. A hypothesis test examines two opposing hypotheses about a population: the null hypothesis and the alternative hypothesis. Null hypothesis: usually refers to a general statement or default position that there is no relationship between two measured phenomena, or no difference among groups. Alternative hypothesis: opposite of the null; there is a difference or a relationship between measured phenomena or groups For example, a study is designed in order to test whether there is a difference in patient satisfaction among two groups. Patient satisfaction is better in patients cared for solely by a registered nurse as compared to patient satisfaction in patients cared for by a team of caregivers. You are testing this hypothesis so let’s look at the null and alternative hypotheses. Null Hypothesis: there is no difference in patient satisfaction. Alternative hypothesis: there is a difference in patient satisfaction In order to test this hypothesis, the researcher sets the level of significance (alpha). In most nursing research the level of significance is set to .05. The number alpha is the threshold value that we measure p values against. It tells us how extreme observed results must be in order to reject the null hypothesis of a significance test. The alpha value gives us the probability of a type I error. Type I errors occur when we reject a null hypothesis that is actually true (false positive). Type II errors occur when a researcher fails to reject a false null hypothesis (a "false negative", i.e., rejecting a true hypothesis as incorrect. p-value: probability; every test statistic or hypothesis has a corresponding probability or p-value. This value is the probability that the observed statistic occurred by chance alone, assuming that the null hypothesis is true. Statistical Significance Produced with a Trial Version of PDF Annotator - www.PDFAnnotator.com To determine if an observed outcome is statistically significant, we compare the values of alpha and the p -value. There are two possibilities that emerge: The p-value is less than or equal to alpha. In this case, we reject the null hypothesis. When this happens, we say that the result is statistically significant. In other words, we are reasonably sure that there is something besides chance alone that gave us an observed sample. The p-value is greater than alpha. In this case, we fail to reject the null hypothesis. When this happens, we say that the result is not statistically significant. In other words, we are reasonably sure that our observed data can be explained by chance alone. Using the following chart, determine whether to accept or reject the null hypothesis and what type of error may occur based on the p-values. Plac your answers in the multiple fill in the blank quiz. Use only the words ACCEPT OR REJECT and I or II (Upper-case I (eye) only) in your answers. Do not add the word "TYPE". Any other words or misspellings will be counted wrong. No late assignments accepted in Week 5. p-value alpha null potential error This quiz was locked Feb 15 at 11:59pm. Attempt History Attempt Time Score LATEST Attempt 1 1 minute 100 out of 100 Score for this quiz: 100 out of 100 hypothesis 1. .015 .01 accept or reject Type I or Type II 2. .01 .01 accept or reject Type I or Type II 3. .009 .01 accept or reject Type I or Type II 4. .061 .05 accept or reject Type I or Type II 5. .022 .05 accept or reject Type I or Type II 6. .05 .05 accept or reject Type I or Type I