TEST_MEAN - Population Mean Test

1. The sample data may include missing values (e.g. #N/A).
2. The test hypothesis for the population mean:
   $$H_{o}: \mu=\mu_o$$
   $$H_{1}: \mu\neq \mu_o$$
   Where:
   
   • $H_{o}$ is the null hypothesis.
   • $H_{1}$ is the alternate hypothesis.
   • $\mu_o$ is the assumed population mean.
   • $\mu$ is the actual population mean.
3. For the case in which the underlying population distribution is normal, the sample mean/average has a Student's t with T-1 degrees of freedom sampling distribution:
   $$\bar x \sim t_{\nu=T-1}(\mu,\frac{S^2}{T}) $$
   Where:
   
   • $\bar x$ is the sample average.
   • $\mu$ is the population mean/average.
   • $S$ is the sample standard deviation.
     $$ S^2 = \frac{\sum_{i=1}^T(x_i-\bar x)^2}{T-1}$$
   • $T$ is the number of non-missing values in the data sample.
   • $t_{\nu}()$ is the Student's t-Distribution.
   • $\nu$ is the degrees of freedom of the Student's t-Distribution.
4. The Student's t-Test for the population mean can be used for small and for large data samples.
5. This is a two-sides (i.e. two-tails) test, so the computed p-value should be compared with half of the significance level ($\alpha/2$).
6. The underlying population distribution is assumed normal (gaussian).

Example 1:

Files Examples

Related Links

• Wikipedia - Statistical hypothesis testing
• Wikipedia - Student's t-test
• Wikipedia - Student's t-distribution

References

• George Casella; Statistical Inference; Thomson Press (India) Ltd; (Dec 01, 2008), ISBN: 8131503941
• K.L. Lange, R.J.A. Little, and J.M.G. Taylor. "Robust Statistical Modeling Using the t Distribution." Journal of the American Statistical Association 84, 881-896, 1989