1 / 13

Analyzing Statistical Inferences

Analyzing Statistical Inferences. July 30, 2001. Inferential Statistics? When?. When you infer from a sample to a population Generalize sample results to the larger group. Sampling Error. Different samples = different means Must take error into account when inferring to population

loren
Download Presentation

Analyzing Statistical Inferences

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. Analyzing Statistical Inferences July 30, 2001

  2. Inferential Statistics?When? • When you infer from a sample to a population • Generalize sample results to the larger group

  3. Sampling Error • Different samples = different means • Must take error into account when inferring to population • What if population is the sample? • Not sampling error • Measurement error

  4. Null Hypothesis • Statement of no difference or no relationship. • Because of sampling error, it is more accurate to test for no differences/relationships • Use statistics to determine the probability that the null hypothesis is true or untrue.

  5. Level of Significance • The probability of being wrong in rejecting the null hypothesis. • p .05 or p .01 • p indicates how often the results would be obtained because of chance.

  6. Type I Error • Reject the null hypothesis when it is true. • Claim a relationship between variables that does not exist.

  7. Type II Error • Fail to reject the null hypothesis when it is not true. • Do not indicate a relationship between variables when there is one.

  8. Three Factorsaffecting the level of significance • Difference between groups • Greater difference, lower p value • Sampling/measurement error • Lower error, lower p value • Sample size • Larger sample, lower p value

  9. What Does it Mean? • If null hypothesis is rejected or not: • Extraneous variables? • Design factors? • Internal validity? • Statistical significance not necessarily practically significant.

  10. Procedures • Parametric statistics • Based upon certain assumptions about the data (i.e. normally distributed) • Nonparametric statisitics • Assumptions about the data cannot be met. • Parametric have greater power to detect significant differences.

  11. Common Procedures • The t test • Compares two means • ANOVA • Compares two or more means • Factorial Analysis of Variance • Two or more independent variables analyzed together

  12. Common Procedures • ANCOVA • Adjusts for pretest difference between groups • Univariate • One dependent variable analyzed • Multivariate • Two or more dependent variables analyzed together

  13. Common Procedures • Chi-square • Tests frequency counts in different categories

More Related