FANR 3000 1st Edition Lecture 8Outline of Last Lecture I. Data Interpretation and T-table II. Confidence intervals Outline of Current Lecture I. HypothesesII. Type I and Type II ErrorsIII. P-values Current LectureI. Hypotheses - Inferential Statistics o To estimate a population parametero To test a hypothesis - The assumption made about the population parameters1. Determine null (H0) and alternative hypothesesa. H0- the belief that the mean of the population is <,>, or = a specific valueb. H1- opposite of the null hypothesis; holds true if H0 is found to be false c. Results of test will either bei. Reject null hypothesisii. Fail to reject null hypothesis II. Type I and Type II Errors2. Specify level of significance a. Type I Error: a false positive; rejection of H0 when in reality it is true b. Type II Error: a false negative; failure to reject H0 when in reality it is not true - Increasing the sample size will reduce both types of error 3. Select the test statistic that will be used 4. Collect the sample data and compute the value of the test statistic III. P-values5. Use value of the test statistic to make a decision using a p-value These notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.a. P-values: the smallest level of significance at which the H0 will be rejected, assuming the H0 is true; the probability that the observed statistic occurred by chance alone b. To determine if the observed outcome is statistically significant, compare p-value to acceptable probability (α)i. If p-value <α, reject H0ii. If p-value >α, fail to reject H06. Interpret statistical results in “real world”