PSYCH 210 1st Edition Lecture 1Outline of Last Lecture I. Overview of SyllabusII. Dog Agility Video Clipa. Class-formulated Statistics questionsOutline of Current Lecture I. Descriptive vs. Inferential StatisticsII. Populations and SamplesIII. Terminology and NotationIV. Types of VariablesCurrent LectureI. Descriptive Statisticsa. Def: procedures used to describe, organize and summarize data b. Most basic type of statistics; no manipulation of data, just ‘what do we have?’c. Ex) Average course time of dog agility route?i. Average = Mean, which is a form of Central Tendencyd. Ex) Most common type of faults dogs make?i. Most Common = Mode, form of Central Tendencye. Percent of dogs in each height category?i. Percent = Summary Statisticf. What is the range of ages of dogs that compete?i. Range (spread of scores) = Variability Statistic1. Variability Statistics Include:a. Rangeb. Standard Deviationc. Varianceg. Is there a correlation between speed and accuracy?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. Correlation:Dogs YPS (Yards per sec) Number of Faultsx yII. Inferential Statisticsa. Def: Procedures that allow us to draw conclusions or make inferences that extend beyond the immediate data at handb. Useful for comparison of groupsc. Ex) What is the Influence of the size of audience on dog performance?Small Audiencen=20Large Audiencen=20Average Course time45 sAverage Course Time36 sd. Inference: making larger (population) conclusion from small sampleIII. Populations vs. Samplesa. Population Def: The complete set of ALL individuals (or measurements) sharing some common, observable characteristicb. Sample Def: a subset of observations drawn from a populationi. We use samples because unless the population is SUPER tiny, entire populations are too big, expensive, etc. ii. Experiment SampleConclusionInference Whole Populationiii. We must rely on samples, but we want to know about the entire population1. Therefore, Random Sampling Extremely Important!iv. Random Sampling 1. Def: Every Individual in a Population has an equal probability of being selected for a sample2. Purpose: avoid bias (because we want to ensure/guarantee that our sample is representative of the population)3. Perfect Random Sample is very difficult to executeIV. Terminology and Notationa. Parameter Def: Value that refers to a numerical property of a populationb. Statistic Def: Value that refers to a numerical property of a samplePopulation SampleMean μ MVariance σ2s2V. Variablesa. Independent Variablesi. Def:1. Manipulated by the experimenter a. Ex) Size of Audienceb. IV = Size of Audience >>> (Overall Category)Levels: small, large >>> (Actual Groups)-OR-2. How participants are groupeda. IV = Age of DogLevels: Young, Middle, Oldb. Dependent Variablesi. Def: measured variableii. Ex) # of faults, # of trials to criterion, course timec. Attribute Variablesi. Def: Variable the experimenter cannot choose/doesn’t have control over1. Ex) Inherent characteristics of individuals i.e. genderii. Comparison:IV = Size of AudienceLevels: small, largeIV = Gender of DogLevels: male, femaleDV = Course Time DV = Course Timeiii. How do you discern if it is an Independent Variable (IV) or Attribute Variable (AV)?1. Random Assignment TestAttribute or Subject Variable (experimenter cannot change)True Independent Variable (completely manipulated by theexperimenter)a. Random Assign Def: Each Individual has an equal chance ofgetting into any group (level)i. If you can randomly assign participants to groups = true IVii. If you cannot randomly assign = AVb. Ex) Gender of suspect in video participants view i. Gender = IVLevels: M, F suspectc. Ex) Gender of participant watching videoi. Gender = AVLevels: M, F participantd. Variables can be used in three different ways in researchi. Correlational Method1. Investigating relationship between two DVs (no IV)a. Correlation does NOT equal causation! 2. Experimental Method3. Quasi-Experimental