1. Hypothesis Testing

2. Intro to Hypothesis Testing Make a conjecture and test its validity Null hypothesis, H o : Make a conjecture about a population statistic Alternative hypothesis, H a : Accepted when the null hypothesis is rejected A test statistic is computed from sample data and tested to see if it falls in the rejection region

3. Testing Errors We would like for the test to work properly, i.e. if the conjecture is true the test indicates so, and if it is false, the test indicates that Type I Error: This is rejecting the null hypothesis when it is in fact true Type II Error: This is when the null hypothesis is not rejected when it is actually false

4. Graphical Interpretation

5. Possible Test Outcomes

6. Example 4.5 EXAMPLE OF TESTING ERRORS ASSUME: 1. Population of 10,000 people. 2. Test for flu virus that has 95% confidence level. 3. 9200 test negative for flu 4. 800 test positive. Then: 1. Type I error: people who test positive for flu but do not have it: 800 × 0.05 = 40 2. Type II error: people who have flu but test negative for it are: 9200 × 0.05 = 460. Thus = 460/10,000 = 0.046 or 4.6% 460 + 40 = 500 people incorrectly test for flu. Or 5% of the population.

7. Type I and Type II Errors We can fix the probability of the Type I error: it is α Generally, we can not determine the probability of the type II error The probability of the type II error decreases with increasing sample size or with increasing probability of the type I error

8. Outcomes of Hypothesis Test Reject the null hypothesis Do not reject the null hypothesis Caution – not rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true

9. Guilty vs Not Guilty In the US, a person charged with a crime is assumed innocent until proven guilty. The null hypothesis is person=innocent. It is our custom (based on the Constitution) to use a small α (low probability of type I error) which makes it unlikely that an innocent person will be wrongly convicted. The trade-off is that the probability of the type II error increases and many guilty people will go free.

10. Types of Tests – One-Tail ONE-TAIL TEST: Equivalent to checking if test statistic is only greater or less than critical value. Only concerned with one tail of distribution. That is, either greater or less than critical value. Typical null hypothesis, H o : Statistic1 = Statistic2 Typical alternative hypothesis, H a : Statistic1 > Statistic2 or Statistic1 < Statistic2

11. Types of Tests – Two-Tail TWO-TAIL TEST: Equivalent to confidence interval. Either test statistic in region or outside of region. Typical null hypothesis, H o : Statistic1 = Statistic2 Typical alternative hypothesis, H a : Statistic1 ≠ Statistic2

12. Test Concerning Population Mean One-tail test is used to see if a population mean is greater than (or less than) a conjectured value The null hypothesis in a one-tail test is stated as an equality, but it makes more sense (in Bon’s opinion) to make it an inequality The two-tail test is used to see if a population mean is different from a conjectured value

13. Hypothesis Test for Mean One-tailed testTwo-tailed test Test Statistic Rejection Region Null hypothesis Alternative hyp.

14. ←not appropriate

15. Better Interpretation The question is whether the EDM is properly calibrated or not. The baseline of calibrated length 400.008m, is considered a fact. This hypothesis can be made before going to the calibration range. H o : μ=400.008 H a : μ≠400.008 8.94>2.093, therefore reject null hypothesis

16. Hypothesis Test for Variance One-tailed testTwo-tailed test Test Statistic Rejection Region Null hypothesis Alternative hyp.

17. Example 4.7 The owner of a surveying firm wants all surveying technicians to be able to read a particular theodolite to within ±1.5". To test this value, the owner asks a senior field crew chief to perform a reading test with the instrument. The crew chief reads the plates 30 times, and obtains S=0.9”. Does this support the 1.5“ limit at a 5% significance level? (use one-tail test) H o : σ 2 ≤ (1.5”) 2 H a : σ 2 > (1.5”) 2 10.44<42.56 so don’t reject null

18. Example 4.7 - revised The owner of a surveying firm wants all surveying technicians to be able to read a particular theodolite to within ±1.5". To test this value, the owner asks a senior field crew chief to perform a reading test with the instrument. The crew chief reads the plates 30 times, and obtains S=0.9”. Does this support the 1.5“ limit at a 5% significance level? (use one-tail test) H o : σ 2 ≥ (1.5”) 2 H a : σ 2 < (1.5”) 2 10.44<17.71 so reject null This is a much more definitive statement.

19. Comments on Example “This example illustrates an important point to be made when using statistics. The interpretation of statistical testing requires judgment by the person performing the test. It should always be remembered that with a test, the objective is to reject and not accept an hypothesis.” Wolf and Ghilani, p. 75

20. Hypothesis Test for Variance Ratio One-tailed testTwo-tailed test Test Statistic Rejection Region Null hypothesis Alternative hyp.

21. Example 4.8 Minimally constrained trilateration network with 24 degrees of freedom has a reference variance, S o 2 = 0.49. With full control constraint, S o 2 = 2.25. Are the two reference variances different at a 0.05 level of significance? 4.59>2.21, therefore reject null hypothesis (reference variances are different)

22. Example 4.9 Ron and Kathi are comparing precision (i.e. variance). With 50 degrees of freedom each, Kathi’s variance is 0.81 and Ron’s variance is 1.21. Is Kathi’s precision better at a 0.01 level of significance? One-tail test 1.49 is not > 1.95 so Kathi is not better How about at 0.1 level of significance? 1.49 > 1.44 so Kathi is better

23. Example 4.9 - Extended It is possible to determine the level of α at the point of rejection through trial and error using STATS (not with tables). Keep entering different values of α until the F value is 1.49. That’s why a 0.01 level does not indicate Kathi is better (nor 0.05), but at a 0.10 level the test shows she is better.