KIN 4310 1nd Edition Exam 2 Study Guide Lectures 9 15 Lecture 9 February 24 Validity is the extent to which inferences made from a certain type of test are appropriate meaningful and useful So basically a valid test is a test that measures what it says it is supposed to measure There are 3 special types of validity that we discussed Content Validity o This is when you want to know whether a sample of items truly reflects an entire universe of items in a certain topic o Ex Driving test first aid certification college quizzes and exams Criterion Validity o This is when you want to know whether test scores are able to predict one s competence in a certain area o Ex SATs predict college GPA Construct Validity o This is when you want to know whether a test measures some type of psychological construct like honesty intelligence and depression for example Reliability is the extent to which a measurement is free of error It is how close the observation is to the true score Validity and Reliability are two different concepts but they are related A test can be reliable and not valid Also a test cannot be valid until it is reliable because a test cannot do what it is supposed to do validity until it does what it is supposed to do consistently reliability Lecture 10 February 26 The z score is the number of standard deviations that a given value x is above or below the mean It is a commonly used standard score It allows us to compare and interpret any distribution but it can ONLY be calculated from interval and ratio scores only A z score between 2 and 2 is an ordinary value A z score 2 or 2 is an unusual value Z score formula for a sample Z score formula for a population Percentiles and Quartiles are used to summarize sets of data Percentiles o There are 99 percentiles denoted by P1 P2 P3 P99 which partition into 100 groups o Ex On the GRE if you scored in the 90th percentile you did better than 90 of the other people that took the test Quartiles o Q1 separates the bottom 25 from the top 75 o Q2 is the same as the median it separates the bottom 50 from the top 50 o Q3 separates the bottom 75 of from the top 25 Lecture 11 March 3 Probability represents how likely a specific event is to occur It is the number of desired outcomes divided by the total number of possible outcomes In statistics probability is used to state how confident we can be about the existence of a particular statistical relationship The probability of an impossible event is the probability of an event that is certain to occur is 1 A smaller probability value means that we are less confident that the observation is real A bigger probability value means that we are more confident that the observation is real Rare Event Rule is when data occurs that is extremely improbable and we must question the assumption that it occurred randomly An example is the card trick and the all male jury being selected in a case involving women issues A Formal Hypothesis begins with a claim that is similar to a hypothesis this is H1 Then there is the skeptic s choice that is the negation of the claim this is HO Lecture 12 March 5 Positive versus Negative Results Rejecting HO is a positive result Not rejecting HO is a negative result A Test Statistic is a value that comes from your sample data and it is used to test the null hypothesis It describes how extreme your data is Some examples include z score r P The steps for the Scientific Method include Assume that HO is true Select an appropriate sample Perform a test Collect data Given that HO is true is it likely that you would end up with the data you got o If yes then you fail to reject HO o If no then you reject HO The Significance Level is denoted by alpha and is the probability representing how rare must a test statistic be in order to be able to reject the null hypothesis In our case alpha will be equal to 0 05 The Critical Value is a value of the test statistic that is used to determine the result of the hypothesis test If the test statistic has a smaller value than the critical value the null hypothesis will be rejected If the test statistic has a bigger value than the critical value the null hypothesis will fail to be rejected The p value is the probability of getting a value more extreme than the test statistic by random chance assuming that the null hypothesis is actually true If the p value is less than alpha we reject the null hypothesis A small p value means strong evidence so it is unlikely the events occurred by random chance A big p value means weak evidence so it is not strong enough to reject Reject HO if the p value is or equal to alpha o This is the red zone Fail to reject HO if the p value is than alpha o This is the blue zone Lecture 13 March 10 Non Directional or Two tailed Test Right tailed Test Left tailed Test A Type 1 Error is the mistake of rejecting the null hypothesis when it is really true This is a false positive The symbol alpha is used to represent the probability of a type 1 error Use a large n to help reduce errors A Type 2 Error is the mistake of failing to reject the null hypothesis when it is false This is a false negative The symbol beta is used to represent the probability of a type 2 error Use a large n to help reduce errors Type 1 and Type 2 Errors Chart A One Sample Z Test is used to compare a sample to a population Here we are trying to find if the difference is significant Lecture 14 March 12 The t test is used to determine if there is a significant difference between two groups Here we are trying to find if the results are statistically significant Table B2 gives us the critical values of the t statistic Excel functions were also introduced in this lecture Lecture 15 March 24 This lecture was a review over the section 2 materials 1 Validity is that the application of a test result is appropriate meaningful and useful 2 Criterion Validity is when you measure one thing to relate to another thing An example is skin fold calipers the GRE accurately predicts whether a student will successful in grad school Content Validity is the extent to which the tool covers the universal item it is intended to construct An example is the texas bar exam covers all the key knowledge that an attorney must have An example of Construct Validity is the depression inventory is a good test for diagnosing clinical depression 3 A z score is a standard …