Hypothesis test
Statement about the value of a population.
Significance Level and Confidence Level
Significance level is the statistical error rate (e.g. 5%)
The confidence level is 1 Less the Significance level
Hypothesis Testing Procedure
1) State the hypothesis
2) Select a test statistic
3) Specify the level of signifcance
4) State the decision rule for the hypothesis
5) Collect the sample and calculate statistics
6) Make a decision about the hypothesis
7) Make a decision based on the test results
Two-Tailed Test
Use when testing to see if a population parameter is different from a specified value.
One-tailed test
Use when testing to see if a parameter is above or below a specified value
Z Statistic
(\bar(x) - \mu)/(\sigma / (n^.5)
t-statistic
(\bar(x) - \mu)/(s/ (n^.5)
Type 1 error
Rejecting a true null hypothesis
Type 2 error
Failing to reject the false null hypothesis
p-value
The smallest level of significance at which the null can be rejected
Types of hypothesis tests: Value of a population mean
Use a t-test or z-test if sample size is large enough.
Types of hypothesis tests: Equality of two population means
Use a t-test
Types of hypothesis tests: Population Variance
Chi-square test
Types of hypothesis tests: Two population variances
F-test
Difference between means: Test Statistic
[\bar(x1) - \bar(x2)] - [\mu(x1) - \mu(x2)]/[s^2/n1 - s^2/n2]^.5
Chi-square test statistic
X^2 = (n-1)S/\sigma^2
F-test statistic
F = S1^2/S2^2
df are n -1
Parametric
Based on assumptions about population distributions and population parameters
Non-parametric tests
Assumptions about the population distribution and test things other than parameter values