Biostat Chapter 9-11 Flashcards
two forms of statistical inference
- hypothesis testing
2. estimation
Hypothesis tests
(also called significance test) are used to assess evidence against a claim
used to test the validity of a claim that is made about a population
Hypothesis testing steps
- convert the research question into statistical null (Ho) and alternative hypotheses (Ha)
- Calculate the appropriate test statistics
- Convert the test statistics to a P-value
- Consider the significance level of the results
- Formulate a conclusion in the context of the data and research question
Main “output” of hypothesis testing
P-value
P-value
Significance levels show you how likely a result is due to chance
chance of rejecting a true null hypothesis (Ho)
statistically significant
results are significant if they are unlikely to have happened by chance.
The most common level, used to mean something is good enough to be believed, is .95. This means that the finding has a 95% chance of being true. However, this value is also used in a misleading way. No statistical package will show you “95%” or “.95” to indicate this level. Instead it will show you “.05,” meaning that the finding has a five percent (.05) chance of not being true, which is the converse of a 95% chance of being true.
Required conditions for Z-test
- SRS
- Normal population
- Know population standard deviation
- Data is accurate
Type I error
Rejecting a true null hypothesis
Type II error
Accepting a false null hypothesis
Second form of statistical inference
estimation
point estimator
Sample mean (x̅) is the point estimator of population parameter (μ)
Interval Estimates
AKA confidence intervals.
Common CI’s are 90%, 95% and 99%
Conditions for z confidence interval procedure for μ
- SRS
- Normal distribution
- σ
- data is accurate
What “significance” means in statistics rather than in English
In normal English, “significant” means important, while in Statistics “significant” means probably true (not due to chance).
