Evaluating VR Systems. Scenario You determine that while looking around virtual worlds is natural and well supported in VR, moving about them is a difficult.

Slides:



Advertisements
Similar presentations
Inferential Statistics and t - tests
Advertisements

Review bootstrap and permutation
Significance Testing.  A statistical method that uses sample data to evaluate a hypothesis about a population  1. State a hypothesis  2. Use the hypothesis.
Chapter 9 Hypothesis Testing Understandable Statistics Ninth Edition
CHAPTER 21 Inferential Statistical Analysis. Understanding probability The idea of probability is central to inferential statistics. It means the chance.
Population Sampling in Research PE 357. Participants? The research question will dictate the type of participants selected for the study Also need to.
Statistical Significance What is Statistical Significance? What is Statistical Significance? How Do We Know Whether a Result is Statistically Significant?
HYPOTHESIS TESTING Four Steps Statistical Significance Outcomes Sampling Distributions.
Chapter 9 Hypothesis Testing Testing Hypothesis about µ, when the s.t of population is known.
Evaluating Hypotheses Chapter 9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics.
Statistical Significance What is Statistical Significance? How Do We Know Whether a Result is Statistically Significant? How Do We Know Whether a Result.
Evaluating Hypotheses Chapter 9 Homework: 1-9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics ~
PSY 1950 Confidence and Power December, Requisite Quote “The picturing of data allows us to be sensitive not only to the multiple hypotheses that.
Statement of the Problem Goal Establishes Setting of the Problem hypothesis Additional information to comprehend fully the meaning of the problem scopedefinitionsassumptions.
AP Experimental Methodology
Chapter 9 For Explaining Psychological Statistics, 4th ed. by B. Cohen 1 What is a Perfect Positive Linear Correlation? –It occurs when everyone has the.
Inferential Statistics
Choosing Statistical Procedures
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 12 Analyzing the Association Between Quantitative Variables: Regression Analysis Section.
Testing Hypotheses I Lesson 9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics n Inferential Statistics.
1 © Lecture note 3 Hypothesis Testing MAKE HYPOTHESIS ©
© 2011 Pearson Prentice Hall, Salkind. Introducing Inferential Statistics.
Tuesday, September 10, 2013 Introduction to hypothesis testing.
Chapter 8 Introduction to Hypothesis Testing
Claims about a Population Mean when σ is Known Objective: test a claim.
Conducting a User Study Human-Computer Interaction.
Statistical Analysis A Quick Overview. The Scientific Method Establishing a hypothesis (idea) Collecting evidence (often in the form of numerical data)
Chapter 9 Hypothesis Testing II: two samples Test of significance for sample means (large samples) The difference between “statistical significance” and.
The Scientific Method in Psychology.  Descriptive Studies: naturalistic observations; case studies. Individuals observed in their environment.  Correlational.
1. Researchers use the terms variable, subject, sample, and population when describing their research. 2. Psychologists do research to measure and describe.
Chapter 1 Measurement, Statistics, and Research. What is Measurement? Measurement is the process of comparing a value to a standard Measurement is the.
Experimental Design Experiment: A type of research study that tests the idea that one variable causes an effect on another variable.
Research Methods In Psychology Mrs. Andrews. Psychology… The scientific study of behavior and mental processes.
1 ConceptsDescriptionHypothesis TheoryLawsModel organizesurprise validate formalize The Scientific Method.
Section 9.3 ~ Hypothesis Tests for Population Proportions Introduction to Probability and Statistics Ms. Young.
Essential Question:  How do scientists use statistical analyses to draw meaningful conclusions from experimental results?
S-012 Testing statistical hypotheses The CI approach The NHST approach.
Economics 173 Business Statistics Lecture 4 Fall, 2001 Professor J. Petry
Tests of Significance: The Basics BPS chapter 15 © 2006 W.H. Freeman and Company.
©2010 John Wiley and Sons Chapter 2 Research Methods in Human-Computer Interaction Chapter 2- Experimental Research.
Example Suppose we want to prove that the mean commute time for people in Lexington to get to work is less than 20 minutes. A random sample of 125 people.
: An alternative representation of level of significance. - normal distribution applies. - α level of significance (e.g. 5% in two tails) determines the.
Review I A student researcher obtains a random sample of UMD students and finds that 55% report using an illegally obtained stimulant to study in the past.
Experimental Research Hanser and Wheeler. Principles Independent Variable Dependent Variable.
Inen 460 Lecture 2. Estimation (ch. 6,7) and Hypothesis Testing (ch.8) Two Important Aspects of Statistical Inference Point Estimation – Estimate an unknown.
What is a Test of Significance?. Statistical hypotheses – statements about population parameters Examples Mean weight of adult males is greater than 160.
CHAPTER OVERVIEW Say Hello to Inferential Statistics The Idea of Statistical Significance Significance Versus Meaningfulness Meta-analysis.
Statistical Inference Statistical inference is concerned with the use of sample data to make inferences about unknown population parameters. For example,
Introduction to Statistical Methods By Tom Methven Digital slides and tools available at:
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 12 Tests of Goodness of Fit and Independence n Goodness of Fit Test: A Multinomial.
Hypothesis Testing and Statistical Significance
Chapter 22 Inferential Data Analysis: Part 2 PowerPoint presentation developed by: Jennifer L. Bellamy & Sarah E. Bledsoe.
15 Inferential Statistics.
Chapter 8 Introducing Inferential Statistics.
Research Methodology Lecture No :25 (Hypothesis Testing – Difference in Groups)
Unit 3 Hypothesis.
P-values.
Chapter 9 Hypothesis Testing
Hypothesis Testing II: The Two-sample Case
Understanding Results
Hypothesis Testing and Confidence Intervals (Part 1): Using the Standard Normal Lecture 8 Justin Kern October 10 and 12, 2017.
Two-sided p-values (1.4) and Theory-based approaches (1.5)
Statistical Inference for the Mean Confidence Interval
Statistical inference
Hypothesis Tests for Proportions
Chapter 10 Analyzing the Association Between Categorical Variables
Testing Hypotheses I Lesson 9.
CS 594: Empirical Methods in HCC Experimental Research in HCI (Part 1)
Statistical inference
Presentation transcript:

Evaluating VR Systems

Scenario You determine that while looking around virtual worlds is natural and well supported in VR, moving about them is a difficult problem. You address this problem by developing a new locomotion technique for virtual worlds. What now? – Prove that your design is better than alternatives – What’s “better” and how do you “prove” it?

Better how? Usability – Intuitiveness, flexibility, functionality Presence/Copresence – Pi, Psi Performance – Accuracy, precision, efficiency Effectiveness – Training, therapy, distraction

Proof Compare system with alternative(s) by conducting human subjects experiments (user studies) How do we go about this “scientifically”? – Design an experiment to identify and potentially magnify differences you expect to exist between systems? – Is the experiment a good (valid one)?

Classic VR Experiment Recruit participants from the local population (population sample) Randomly divide that sample into two groups, control and intervention. Control group gets normal VR, and intervention group gets new VR Compare observations of the two groups to determine if they are significantly (non- randomly) different

Experimental Design Validity Internal Validity – Does your design properly address possible bias factors that may lead to incorrect interpretation of observations? E.g. Selection bias – Can you definitively establish a cause and effect relationship? Correlation is not causation External Validity – Generalization of results to other settings

Observations (Measures) Constructs must be operationalized as measures (metrics). Data produced by the metric proportional to hypothetical value of the construct Construct Validity – “Are you measuring what you think you’re measuring?” – Very difficult to establish (Think Pi, Psi) – Requires community to believe you – Comes after having reliability, predictive validity

How do you compare the data? Assume (Hypothesize) that all of the data comes from the same exact population. – Null Hypothesis – Alternative Hypothesis (your real belief) Find the likelihood of that seeing your particular distribution with random samples of that data If the probability of seeing your particular distribution is less than some pre-determined value, REJECT your hypothesis.

Statistical Tests Most common in VR, by far, is the Student’s T- Test (built into most spreadsheet software) – Can be used to determine the probability that a sample population has a specific mean – Can be used to determine if two samples have the same mean Compute T-Value for your case Determine probability of seeing this or greater T value in the T distribution

Example Suppose you have Metric M, which yields the following values for group 1 and group 2 – [1,7,5,6,4,1,8,5,6,3,7,5,6,4,6] – [2,1,6,1,3,2,1,7,3,1,5,3,8,4,1] Is there a significant difference between the two groups?