1 / 35

Introduction to Hypothesis Testing

Introduction to Hypothesis Testing. CJ 526 Statistical Analysis in Criminal Justice. Hypotheses. A hypothesis is a prediction about the outcome of a research study. Hypothesis Testing.

montague
Download Presentation

Introduction to Hypothesis Testing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Introduction to Hypothesis Testing CJ 526 Statistical Analysis in Criminal Justice

  2. Hypotheses • A hypothesis is a prediction about the outcome of a research study

  3. Hypothesis Testing • Hypothesis testing is an inferential procedure that uses sample data to evaluate the credibility of a hypothesis about a population

  4. Overview of Hypothesis Testing • State a hypothesis about a population • Usually in terms of the value of a population parameter • Typically the mean or the difference between means

  5. Overview of Hypothesis Testing -- Continued • If the data are consistent with the hypothesis, conclude that the hypothesis was reasonable, and fail to reject it

  6. Example • Babies born to women who smoke during pregnancy will be more likely to be of low birth weight • Independent Variable: • Smoking during pregnancy • Dependent Variable: • Birth weight

  7. Example -- Continued • Obtain a random sample of women who are pregnant and smoke • Weigh the babies at birth • Compare sample data to hypothesis • Make decision: • Reject the hypothesis • Fail to reject the hypothesis

  8. Assumptions Behind Hypothesis Testing • The effect of the Independent Variable (treatment effect) is assumed to: • Add (or subtract) a constant from every individual’s score

  9. The Logic of Hypothesis Testing • Can’t prove hypothesis • Proof requires evidence for all cases

  10. Steps in Hypothesis Testing • Determine the number of samples (groups, conditions) • One • Two • k (three or more)

  11. Steps in Hypothesis Testing -- continued • If there are two or more samples, determine whether they are independent or dependent • Same group (repeated-measures) • Match on some other variable(s) known to influence DV (matched-subjects)

  12. Steps in Hypothesis Testing -- continued • If there is one sample and the Dependent Variable is at the Interval or Ratio Level of Measurement, is the standard deviation of the population (, sigma) known: • If  is known, use a One-Sample z-Test • If  is unknown, use a One-Sample t-Test

  13. Steps in Hypothesis Testing -- continued • Identify the independent variable • Identify the dependent variable and its level of measurement • Identify the population to which inferences will be made

  14. Steps in Hypothesis Testing -- continued • Determine the appropriate inferential statistical test • Number of samples • Nature of samples (if applicable) • Level of measurement of DV • State the null hypothesis • State the alternative hypothesis

  15. Steps in Hypothesis Testing -- continued • State Decision Rule: • If the p-value of the obtained test statistic is less than .05, reject the Null Hypothesis • Use SPSS to compute the obtained test statistic • Make decision • Interpret results

  16. Truncated Steps • State the hypotheses

  17. 1. State the Hypotheses • State the null hypothesis

  18. Null Hypothesis • The null hypothesis predicts that the Independent Variable (treatment) will have no effect on the Dependent Variable for the population

  19. Alternative Hypothesis • The alternative hypothesis predicts that the Independent Variable (treatment) will have an effect on the Dependent Variable for the population

  20. Directional Alternative Hypotheses • Researcher has reason to believe before conducting the test that a difference will lie in a specified direction • Prior research • Theory

  21. Nondirectional Alternative Hypotheses • Researcher has no reason to believe that there will be a difference in a specified direction

  22. 2. Set the Criteria • Because of sampling error, there is likely to be a discrepancy between the sample mean and the population mean

  23. 3. Collect Sample Data • Obtained test statistic

  24. 4. Evaluate the Null Hypothesis • Reject the null hypothesis • If sample data is unlikely to have been drawn from a population where the null hypothesis is true • If the p-value of the obtained test statistic is less than .05

  25. Failure to Reject the Null Hypothesis • Either: • Treatment had an effect, could not demonstrate it • or • Treatment had no effect

  26. Errors in Hypothesis Testing

  27. Type I Error • Committed when H0 is rejected as false although it is true

  28. Type II Error • Committed when H0 is not rejected although it is false

  29. Statistical Power • Probability that the test will correctly reject a false null hypothesis

  30. Power -- Continued • When a treatment effect exists • A study may fail to discover it (Type II error, fail to reject a false null hypothesis) • A study may discover it (reject a false null hypothesis)

  31. Power -- Continued • Reducing alpha (.05 --> .01 --> .001) • Reduces power • Inverse relationship between Type I and Type II errors

  32. Power -- Continued • Some inferential statistical tests are more powerful

  33. Jury’s Decision

  34. Level of Significance • Alpha: probability of committing a Type I error • Reject H0 although it is true • Symbolized by 

  35. Level of Significance • Obtained result attributed to: • Real effect (reject H0) • Chance

More Related