PSYCH 301 1st Edition Lecture 8Outline of Last Lecture I. Null Hypothesis Significance Testing (NHST)II. Statistical significanceIII. One- / Two-Tailed HypothesesIV. Relating P to the Comparison DistributionOutline of Current Lecture V. QuestionsVI. Statistical InferenceVII. Three distributionsVIII. Sampling DistributionIX. QuestionsCurrent LectureI. Questionsa. Is there a difference between p = α and p < α?i. (p = α) you do not reject the Null hypothesisii. (p < α) you reject the Null hypothesisb. If you made a one-tailed hypothesis but your results are very far from the mean in the opposite direction. Should you reject or not reject the null hypothesis?i. You would not reject the null hypothesis because you chose a critical value on the wrong end of the spectrum. c. If we want to set our cutoff to α = 0.05, what happened to its corresponding cutoff score(s) when we change out hypothesis from two-tailed to one-tailed?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.i. You don’t have to be as far away from the population mean to stay within the cutoffII. Statistical Inference- what brings it from sample statistics to population parametersa. Statistics are numbers that describe the sample (mean, median, mode, variance,Standard deviation)b. Parameters are numbers that describe the population distributionsIII. Comparisonsa. We have been comparing one score to a population of scores (comparison distribution)b. Now instead of individual scores, we want to compare means (sampling distribution/sampling distribution of the mean/distribution of means)i. Does our sample compare to the comparison distribution?IV. Three distributionsa. Population distribution – individual scoresb. Sample distribution- one scorec. Sampling distribution- meand. Randomly select from population to come up with your samplee. How likely is it to come up with a sample with the same mean?f. Build up comparison distribution and compare means to the sample distributiong. Based on that sample, make inferences based on the populationV. Sampling Distributiona. What does the sampling distribution tell us?i. It tells us the probability of randomly drawing a sample of size n with a particular meanb. Online tool: www.onlinestatbook.com/stat_sim/sampling_dist/index.htmlc. The mean of the sampling distribution is equal to the population meand. If the means aren’t the same then you haven’t collected enough data yete. How does sample size change scenarios?i. The mean never changes but with a larger sample size you get a smaller standard deviationii. The smaller the sample size the larger the standard deviationf. The sample distribution will always be skinnier than the parent populationg. The standard deviation of the sampling distribution is called “standard error of the mean”/”standard error”/”s.e.”/”s.e.m.”i.ii.h. The shape of parent population has no bearing on the sampling distributioni. The shape of distribution of parent population does NOT matterj. The width depends on the sample sizek. The shape of the sampling distribution is independent of the shape of the parentdistribution (the population)VI. Questionsa. Why do we need different comparison distributions for scores versus sample means?i. To suit what are data isb. What does the sampling distribution tell us?i. How likely it is for a certain sample ‘n’ to happenc. What is the relationship between the population standard deviation (σ) and the standard error of the mean (σm)?i. Sample size linksii. σm=