- 1. Student’s T Test Anthony J. Evans Professor of Economics, ESCP Europe www.anthonyjevans.com (cc) Anthony J. Evans 2019 | http://creativecommons.org/licenses/by-nc-sa/3.0/
- 2. Student’s T-test is useful in three situations 1. Low sample size 2. Unknown population standard deviation 3. Comparing two samples 2
- 3. Low sample size • Use a z test to replicate a normal distribution • But if the sample size is small, the Central Limit Theorem doesn’t hold • Use a t test (i.e. involve degrees of freedom) – DoF: is a measure of the number of independent pieces of information on which the precision of a parameter estimate is based. • William Gosset was hired by Guinness after graduating from Oxford University • Used statistics to find best yielding varieties of barley • Problem: small samples • To keep trade secrets he published under the pseudonym “Student” • Hence “Student’s t-Test” 3
- 4. Unknown population σ • Thus far we’ve assumed the population standard deviation • We could use a t-test, which estimates the population standard deviation from the sample standard deviation • I.e. replace sigma with “s” • Use a T-Table rather than a standard normal distribution table 4 t = x − µ s n
- 5. Comparing two samples • We can also use the t-test to see whether the means of two samples are statistically different from one another • Once you compute the t-value you have to look it up in a table of significance to test whether the ratio is large enough to say that the difference between the groups is not likely to have been a chance finding • P-Value – Usually use 0.5 • Degrees of Freedom – The sample size of both groups combined, minus 2 5 𝑡 = 𝑥 𝑎% − 𝑥 𝑏% 𝑠 𝑎 2 𝑛 𝑎 + 𝑠 𝑏 2 𝑛 𝑏
- 6. Z test vs. T test 6 € z = x − µ0 σ n € t = x − µ0 s n Degrees of Freedom = n-1 For comparing 2 samples = n-2 Student’s T test • Used when you only know the standard deviation of a sample (s) • Used if small sample size • Can also be used for comparing two samples Z test • Used when you know the standard deviation of the population (σ) 𝑡 = 𝑥 𝑎% − 𝑥 𝑏% 𝑠 𝑎 2 𝑛 𝑎 + 𝑠 𝑏 2 𝑛 𝑏
- 7. Unpaired sample T test example • Rosenthal and Jacobson (1968) informed classroom teachers that some of their students showed unusual potential for intellectual gains. Eight months later the students identified to teachers as having potential for unusual intellectual gains showed significantly greater gains performance on a test said to measure IQ than did children who were not so identified. Below are the data for the students in the first grade: 7Source: http://www.indiana.edu/~educy520/sec6342/week_10/ttest_exp.pdf
- 8. Unpaired sample T test example • Calculate the T stat • Find the critical value • Do we reject the null hypothesis? • Yes - the difference in scores is not due to chance variation 8Source: http://www.indiana.edu/~educy520/sec6342/week_10/ttest_exp.pdf 𝑡 = 27.15 − 11.95 12.52 20 + 14.62 20 = 3.54 𝑡0.05, 38 = 2.03
- 9. Paired sample T test example • We want to know if there is a difference in the salary for the same job in Boise, ID, and LA, CA. The salary of 6 employees in the 25th percentile in the two cities is given. 9Source: http://www.cogsci.bme.hu/~ktkuser/KURZUSOK/BMETE47MC38/2015_2016_1/7_The%20t-test.pdf Profession Boise Los Angeles Executive Chef 53,047 62,490 Genetics Counselor 49,958 58,850 Grants Writer 41,974 49,445 Librarian 44,366 52,263 School teacher 40,470 47,674 Social Worker 36,963 43,542
- 11. Solutions 11
- 12. Unpaired sample T test example • Calculate the T stat • Find the critical value • Do we reject the null hypothesis? • Yes - the difference in scores is not due to chance variation 12Source: http://www.indiana.edu/~educy520/sec6342/week_10/ttest_exp.pdf 𝑡 = 27.15 − 11.95 12.52 20 + 14.62 20 = 3.54 𝑡0.05, 38 = 2.03
- 13. • This presentation forms part of a free, online course on analytics • http://econ.anthonyjevans.com/courses/analytics/ 13