Chapter 7: Statistical Issues in Research planning and Evaluation

Slides:



Advertisements
Similar presentations
Introduction to Hypothesis Testing
Advertisements

Unlocking the Mysteries of Hypothesis Testing
Introduction to Hypothesis Testing
Anthony Greene1 Simple Hypothesis Testing Detecting Statistical Differences In The Simplest Case:  and  are both known I The Logic of Hypothesis Testing:
Statistics 101 Class 8. Overview Hypothesis Testing Hypothesis Testing Stating the Research Question Stating the Research Question –Null Hypothesis –Alternative.
Decision Errors and Statistical Power Overview –To understand the different kinds of errors that can be made in a significance testing context –To understand.
Statistical Issues in Research Planning and Evaluation
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.
Research Methods in MIS
Hypothesis Testing Steps of a Statistical Significance Test. 1. Assumptions Type of data, form of population, method of sampling, sample size.
Evaluating Hypotheses Chapter 9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics.
DATA ANALYSIS I MKT525. Plan of analysis What decision must be made? What are research objectives? What do you have to know to reach those objectives?
Statistical Significance What is Statistical Significance? How Do We Know Whether a Result is Statistically Significant? How Do We Know Whether a Result.
Chapter 6 Hypotheses texts. Central Limit Theorem Hypotheses and statistics are dependent upon this theorem.
Hypothesis Testing: Type II Error and Power.
Evaluating Hypotheses Chapter 9 Homework: 1-9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics ~
Understanding Research Results. Effect Size Effect Size – strength of relationship & magnitude of effect Effect size r = √ (t2/(t2+df))
Major Points An example Sampling distribution Hypothesis testing
Chapter 8: Hypothesis Testing and Inferential Statistics What are inferential statistics, and how are they used to test a research hypothesis? What is.
Descriptive Statistics
Introduction to Testing a Hypothesis Testing a treatment Descriptive statistics cannot determine if differences are due to chance. A sampling error occurs.
Overview of Statistical Hypothesis Testing: The z-Test
Hypothesis Testing.
Chapter 8 Introduction to Hypothesis Testing
Chapter 7 Statistical Issues in Research Planning and Evaluation.
1 Power and Sample Size in Testing One Mean. 2 Type I & Type II Error Type I Error: reject the null hypothesis when it is true. The probability of a Type.
Learning Objectives In this chapter you will learn about the t-test and its distribution t-test for related samples t-test for independent samples hypothesis.
1 Lecture note 4 Hypothesis Testing Significant Difference ©
Hypothesis testing and Decision Making Formal aspects of hypothesis testing.
Power of a Hypothesis test. H 0 True H 0 False Reject Fail to reject Type I Correct Type II Power   Suppose H 0 is true – what if we decide to fail.
Module 15: Hypothesis Testing This modules discusses the concepts of hypothesis testing, including α-level, p-values, and statistical power. Reviewed.
Scientific Method Probability and Significance Probability Q: What does ‘probability’ mean? A: The likelihood that something will happen Probability.
AP Statistics Chapter 21 Notes
Introduction to Testing a Hypothesis Testing a treatment Descriptive statistics cannot determine if differences are due to chance. Sampling error means.
Chapter 13 Understanding research results: statistical inference.
Hypothesis Testing Steps for the Rejection Region Method State H 1 and State H 0 State the Test Statistic and its sampling distribution (normal or t) Determine.
Chapter 7 Statistical Issues in Research Planning and Evaluation.
Chapter ?? 7 Statistical Issues in Research Planning and Evaluation C H A P T E R.
Chapter Nine Hypothesis Testing.
Section Testing a Proportion
Power of a test.
Dr. Amjad El-Shanti MD, PMH,Dr PH University of Palestine 2016
Chapter 8: Hypothesis Testing and Inferential Statistics
More about Tests and Intervals
Setting significance levels at the correct level
Statistical Tests - Power
P-value Approach for Test Conclusion
Unlocking the Mysteries of Hypothesis Testing
Chapter 9: Hypothesis Testing
Quantitative Methods PSY302 Quiz Chapter 9
Chapter Nine Part 1 (Sections 9.1 & 9.2) Hypothesis Testing
Chapter 3 Probability Sampling Theory Hypothesis Testing.
Statistical Inference for Managers
P-VALUE.
One Way ANOVAs One Way ANOVAs
More About Tests Notes from
Power of a test.
Power of a Hypothesis Test
Power of a test.
Chapter 8 Making Sense of Statistical Significance: Effect Size, Decision Errors, and Statistical Power.
Sample Mean Compared to a Given Population Mean
Sample Mean Compared to a Given Population Mean
Chapter 9: Testing a Claim
Power of a test.
Power Problems.
Inference as Decision Section 10.4.
Type I and Type II Errors
Statistical Power.
Rest of lecture 4 (Chapter 5: pg ) Statistical Inferences
Presentation transcript:

Chapter 7: Statistical Issues in Research planning and Evaluation SFM 651: Research Methods Dr. Johnson

Probability The odds a certain event will occur Relative frequency – getting it close Coin toss – 50-50 Actuality – 52-48 or 51-49

Alpha α Level of probability set by the experimenter before the study Also called level of significance Usually .05 or .01 Findings due to chance 5% or 1% of the time.

Beta β Magnitude of type II error Acceptance of the Null hypothesis when it is false When Alpha is lower, Beta gets higher.

Meaningfulness Importance of significance of the effect Effect size Difference between means divided by Standard deviation

Power Probability of making the correct decision Reject Null Hypothesis when it is false. The greater the power, the more likely it is that you will see a difference.

Ways to obtain more power Increase sample size # of participants Becomes easier to know what will actually happen with more samples.