Class Notes (1,100,000)

US (490,000)

UC-Davis (10,000)

PHI (60)

PHI 31 (20)

Griesemer James (20)

Lecture 10

Department

PhilosophyCourse Code

PHI 31Professor

Griesemer JamesLecture

10This

**preview**shows half of the first page. to view the full**2 pages of the document.** LECTURE 10: 10/19

SAMPLING AND ESTIMATION

● Sampling Processes

○ Each and every sample will look a little different from the next

○ Each test is a trial

○ Sample: produces a sequence of trials

● Probability of a Sequence: combined probability of getting a result on the first trial and a

result on the second trial and a result on the third trial...

○ Sequences have probabilities, just as trials do

● Random Sampling

○ 2 Key Points:

■ 1) In a very large hypothetical set of sequences of 2 trials, if the

proportion of red in each trial is equal to the proportion in the population

■ 2) There is no correlations between trials

● The LARGER the sample…

○ The less likely your sample will have the exact frequency of Red that is the true

proportion in the population

○ The closer to P(R), your sample frequency of red, f(R), will get

■ Close to 50%, the closer to sample size

○ Use combination rules, OR, to add up the area under the curve

■ AND is if the probabilities were to be stacked up vertically on curve

● Probability Density

○ Standard deviation measures how close Y=f(R) is as an estimate of P(R) in a

way that is independent of sample size

○ ⅔ of the probability density is within 1 standard deviation from the most probable

Y

○ 95% of the probability density is within 2 standard deviations from the most

probably Y

LECTURE 11: 10/22

ESTIMATION AND EVALUATION

###### You're Reading a Preview

Unlock to view full version