1 / 13

Chapter 3

Chapter 3. Normal Curve, Probability, and Population Versus Sample Part 2 Tues. Sept. 3, 2013. Using the Table: From % back to Z or raw scores. Steps for figuring Z scores and raw scores from percentages:

kamea
Download Presentation

Chapter 3

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. Chapter 3 Normal Curve, Probability, and Population Versus Sample Part 2 Tues. Sept. 3, 2013

  2. Using the Table: From % back to Z or raw scores • Steps for figuring Z scores and raw scores from percentages: 1. Draw normal curve, shade in approximate area for the % (using the 50%-34%-14% rule) 2. Make rough estimate of the Z score where the shaded area starts 3. Find the exact Z score using the normal curve table: • look up the % and find its z score (see example)

  3. (cont.) 4. Check that your Z score is similar to the rough estimate from Step 2 5. If you want to find a raw score, change it from the Z score, using formula: x = Z(SDx) + Mx

  4. Probability • Probability • Expected relative frequency of a particular outcome • Outcome • The result of an experiment

  5. Probability • Range of probabilities • Probabilities as symbols • p • p < .05 (probability is less than .05) • Probability and the normal distribution • Normal distribution as a probability distribution • Probability of scoring betw M and +1 SD = .34 (In ND, 34% of scores fall here)

  6. Sample and Population • Population parameters and sample statistics– note the different notation for pop or sample • M for sample is  for population • SD for sample is  for population

  7. Ch 4 – Intro to Hypothesis Testing Part 1

  8. Hypothesis Testing • Procedure for deciding whether the outcome of a study support a particular theory • Logic: • Considers the probability that the result of a study could have come about if the experimental procedure had no effect • If this probability is low, scenario of no effect is rejected and the theory is supported

  9. The Hypothesis Testing Process • Restate the question as a research hypothesis & a null hypothesis • Research hypothesis –supports your theory. • Example? • Null hypothesis – opposite of research hyp; no effect (no group differences). This is tested. • Example?

  10. The Hypothesis Testing Process • Determine the characteristics of the comparison distribution • Comparison distribution – what the distribution will look like if the null hyp is true. • Example? Note – we recognize there will be sampling errors in our sample mean

  11. The Hypothesis Testing Process • Determine the cutoff sample score on the comparison distribution at which the null hypothesis should be rejected • Cutoff sample score (critical value) – how extreme a difference do we need to reject the null hyp? • Conventional levels of significance: p < .05, p < .01 • We reject the null if probability of getting a result that extreme is .05 (or .01…)

  12. Step 3 (cont.) • How do we find this critical value? • Use conventional levels of significance: p < .05, p < .01 • Find the z score from Appendix Table 1 if 5% in tail of distribution (or 1%) For 5%  z = 1.64 • We reject the null if probability of getting a result that extreme is .05 (or .01…) • Reject the null hyp if my sample z > 1.64 • What does it mean to reject the null at .05 alpha level?

  13. The Hypothesis Testing Process • Determine your person’s score on the comparison distribution • Collect data, calculate the z score for your person of interest • Use comparison distribution – how extreme is that score? • Decide whether to reject the null hypothesis • If your z score of interest falls within critical/rejection region  Reject Null. (If not, fail to reject the null) • Rejecting null hypothesis means there is support for research hypothesis. • Example?

More Related