COMM 250 Agenda - Week 12 Housekeeping RP2 Due Wed. RAT 5 – Wed. (FBK 12, 13) Lecture Experiments Descriptive and Inferential Statistics

In-Class Team Exercise # 8 – (Review) First Do as Individuals, then produce a Team Version: 1) Design a Factorial Experiment to answer these questions: Which can be read faster on a web site - plain text (plain black letters on a white background, no links) or text supplemented in some way? What other variables might affect a user’s ability to read text? (Name 3 and then Choose one for Step 2) 2) Draw a table of the design - at least 3 levels of one variable, 2 of another (you choose the second IV) Label the 2 IVs and Label Their Levels 3) Write out 2 Hypotheses (H1, H2): One Predicting Effects of IV 1, the other the Effects of IV 2 4) Declare the DV (It is in your H1, H2) 5) List Two (“People”) Variable you Should “Control for”

Statistics Why Study Statistics ?  Integral to the “Scientific Method”  Seeing the Forest Amid the Trees  Describing (Sports Statistics)  Inferring (Correlates of Cancer)  Predicting (Science, Business)  To Pursue an ‘Objective’ View

Statistics Descriptive Statistics:  a way to summarize data Inferential Statistics:  strategies for estimating population characteristics from data gathered on a sample

Descriptive Statistics Measures of Central Tendency  Used to describe similarities among scores  What number best describes the entire distribution? Measures of Dispersion  Used to describe differences among scores  How much do scores vary?

Descriptive Statistics Measures of Central Tendency  MeanThe Average  MediumThe Middle Score  ModeThe Most Common Score

Measures of Dispersion Range  The Highest & Lowest Scores  Variance  A Measure Of Dispersion Equal To The Average Distance Of The Scores, Squared, From The Mean Of All Scores, Divided By N  Standard Deviation  The Square Root Of The Variance (Dispersion About The Mean, Based In The Original Units)

Inferential Statistics Used For - Estimation  To Extrapolate from a Sample to a Population Significance Testing To Determine the Importance of Observed Differences; e.g., Between Groups or Variables

Using Inferential Statistics for Estimation Purpose  To Estimate (Extrapolate) from a Sample (Statistic) to a Population (Parameter) Reason Why  It Saves Time and Money (Don’t Have to Survey/Measure the Entire Population) A Statistic  Measure of a Sample (E.g., Comm250) on Some Variable E.g., Average (Mean) Height of Comm250 Students A Parameter  A Characteristic of a Population (E.g., Virginia) E.g., Average (Mean) Height of Residents of VA

Inferential Statistics Note: When Statistics are Used for Estimation, this is the “Heart” of Using Statistics to Infer - Statistic  Measure of a Sample (E.g., HSers taking SAT in ‘02) on Some Variable E.g., Average (Mean) SAT score in 2002 E.g., Average (Mean) SAT score Among GMU Freshmen Parameter  A Characteristic of a Population (E.g., All HSers Who Have Taken the SAT) E.g., Average (Mean) SAT Across ALL HSers (50 years?)

Parametric & Non-Parametric Parametric Statistics Stats Used to Establish Attributes of a Population Based on Attributes of a Sample Non-Parametric Statistics  Stats Used ONLY to Describe The Attributes of a Sample  No Generalization Back to Its Population is Attempted

Inferential Statistics Estimation of Population Parameters Assumes:  A Normal Distribution  “Bell-Shaped Curve”  Most Variables ARE Normally Distributed  A Random Sample  Unless Some Form of Random Sampling is Used, the Sample Will NOT be “Representative” of the Population (to Which One Wishes to Generalize)

Significance Testing  Analyzing Sample Data To Test Hypotheses About Populations By Convention, There are Always Two H: The Null (H 0 : No Differences) The Experimental (H 1 : Differences Exist)  Hypotheses  One-Tailed = Directional  Two-Tailed = 2-Directional (Non-Directional)

Testing The Null Hypothesis  Set The Significance Level  Compute The Calculated Value  Compare This To The Critical Value Needed To Reject The Null

Testing Differences  Nominal Data:Chi Square Test  Ordinal Data:Median Test  Interval DataT-Test, ANOVA, & Ratio Data: Regression

Types of Variables Definition: “Concepts that take on 2 or more values” Nominal = Equal Groupings (Gender, Race, Political Party) Ordered = Some Priority or Rank Ordinal Rank Order: (Hottest Days, Top Ten) Interval Equal Intervals: (Temp., IQ, Scale from 1-7) Ratio Interval, with a “True” Zero (Weight; Height)

t-Test and ANOVA The t-test  2 independent samples (groups)  Are the samples different ?  A Different Online Example to TryExample Analysis of Variance (ANOVA)  2 or more levels of a (Discrete) IV  Often Multiple IVs; Single DV  Factorial Designs  Main Effects; Interactions

In-Class Team Exercise # 9 First Do as Individuals, then produce a Team Version: A professor believes that posting “practice quizzes” increases student test scores. He provides online quizzes to class A but not to class B. 1) What is the Research Hypothesis? The Null? 2) Conduct a t-test on the Following Quiz 1 Scores:t-test Scores from Class A: 12, 12, 12, 11, 11, 11, 10, 9 Scores from Class B: 9, 8, 8, 7, 7, 7, 6, 4 3) What Conclusion Can Be Drawn? Deliverable : a written version – showing all work

Review of: In-Class Team Exercise # 9 The answers are on the link on the previous page, or click on this link for answers.this link for answers You will be required to do a similar problem (a t-test and the accompanying questions) on P-RAT 6 and in-class RAT

In-Class Team Exercise # 10 Produce a Team Version only: How does talking on a cell phone affect driving? Design a 3 x 2 Factorial Experiment (draw a Table) You Must Use These IVs: Level of Driving Experience (Pick 3 Levels) Type of Distraction (Pick 3: Cell Phone, Changing CDs, You choose #3) Write out 2 Hypotheses (H1, H2): Your DV should be: MPH deviation from the average speed on the road One Predicting the Effects of Driving Experience One Predicting Differences Due to Type of Distraction Label the 2 IVs and Label Their Levels List Two Other Variables you Should “Control for”