- 67 Views
- Uploaded on
- Presentation posted in: General

Statistics Workshop Principles of Hypothesis Testing J-term 2009 Bert Kritzer

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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Statistics Workshop Principles of Hypothesis TestingJ-term 2009Bert Kritzer

- Inference about populations from samples
- Inference about underlying processes
- Could the observed pattern been generated by a random process?

- Inference about systematic vs. random (“stochastic”) components
Observation = Systematic + Random

- Sampling
- Process

- Observed statistics as random variables

μ = 0.564

mean of means = 0.566

A procedure for drawing conclusions about characteristics in a population or about a process:

- Is it reasonable to conclude that there is a relationship between two variables?
- Is it reasonable to conclude that a populaton parameter exceeds some value?
- Is it reasonable to conclude that the population parameter differs among two or more groups?

Are African-Americans more likely to be stopped by the police?

Is the average number of stops for African-Americans different than that for Whites?

Are African-Americans more likely to be stopped by the police?

Is the average number of stops greater for African-Americans than for Whites?

- Can state the null hypothesis with precision, and that allows us to compute the probability of observing a particular result if the null hypothesis is true
- Logically it is easier to ascertain what is untrue than what is true. If we can dichotomize the possibilities A and B, and then determine that A is not true, it must be the case that B is true.

H0: InnocentHA: Guilty

Legal Decisionmaking:

Pr(Guilty|Evidence)

Hypothesis testing:

Pr(Evidence|Innocent)

- How big of a difference could be explained by random processes such as sampling?
- Depends on sample size and characteristics of the underlying distribution

- How big of a difference is enough that we should care about it?
- Normative/policy question
- Depends on “costs” associated with differences

- Statistical significance refers only to the first of these

- How careful do you want to be in reaching a conclusion about your hypothesis?
- Process will ask whether your null hypothesis can be rejected
- What probability that you incorrectly rejected the null hypothesis are you willing to accept?
- This probability is the significance level

- Is the research hypothesis directional?
- Do you think that the salaries of men are greater on average than the salaries of women?
- Do you think that the salaries paid to African-Americans differ from the salaries paid to Whites?
- Is the coin loaded vs. is the coin loaded toward heads?

- “One-tailed” (directional) vs. “Two-tailed” (nondirectional) hypotheses
- If directional hypothesis is correct, you are more likely to reject null hypothesis with one-tailed test

TYPE I (α error): Rejecting a Null Hypothesis that is in fact true

Set by the “significance level”

TYPE II (β error): Failing to Reject a Null Hypothesis that is in fact false

Depends on the significance level, the sample size, and how wrong the null is

- State research (“alternative”) hypothesis HA
- State null hypothesis H0
- Set decision rule
- “level of significance” Pr(α error)
- Directional or nondirectional (“one-tailed” or “two-tailed”) based on research hypothesis

- Obtain data (set sample size)
- Compute test statistic and get probability of observing it under H0 (“p-value”)
- Make decision whether to reject H0

The probability that a hypothesis test will reject a false null hypothesis

β is the probability of a Type II error

1-β is the “power” of a significance test

- The characteristics of a particular test
- The sample size
- The magnitude of the true effect (difference, regression coefficient, etc.) that we are trying to detect

200 feet vs. 500 feet

A chickadee vs. a bluejay

- If the coin is honest, the probability of a head on any one flip is .5.
- Do we suspect that it is loaded one direction or the other?
- How dishonest do we think it is (i.e., what is the actual probability of a head if that probability is not .5)?
- How big is the sample (number of flips)?

- The binomial distribution
- Two parameters are the probability and the “sample size” (number of flips of the coin)
- Choosing the sample size will affect our ability to make a correct decision about the coin
- The bigger the sample size the better

- Knowing the “alternative hypothesis” (how dishonest the coin is) can help in deciding the sample size

Probability of Outcomes for an Honest

Probabilities shown are probabilities of a more extreme outcome

- Dishonest?
- Loaded toward heads?

- How willing are we to decide that the coin is dishonest (loaded toward heads?) when it is actually honest?
- Some possibilities:
.10

.05

.01

.001

- How sure do we want to be able to reject the null hypothesis if the coin is in fact loaded toward heads?
- Power

- Do we have any idea of how dishonest the coin is?

- We will need 12 or more heads on our 15 flips to reject the null at .05 (one-tailed) level
- According to previous chart, if coin has a true probability of heads of .67, we have a 20% of rejecting the null if our research hypothesis is that coin is loaded toward heads

- Direct comparison to hypothesized value
- t-tests using t distribution
- Z-tests using normal distribution
- occasionally reported as a chi square

- Comparisons to a set of hypothesized values based on a model
- “Goodness-of-Fit” test using chi square

- Reduction in predictive error
- Analysis of Variance using F-ratio