Summary of rbms

Lecture 4

Lecture: Confidence Intervals



What will I learn? What should I know?

- Understand the term “point estimate”
- Understand the central limit theorem and its core consequence:
 that the mean of a data set can be seen as a sample of a normal distribution
 always, if the data are sampled from a normal population distribution
 also when the data are sampled from a non-normal population distribution, if n is
sufficiently high
- Understand the difference and relation between a standard error and a standard deviation
- Understand the calculation and meaning of a confidence interval
- Understand the similarities and differences between statistical tests and confidence intervals


The process of null hypothesis testing

Inferential statistics:

1. A statistical test: procedure to decide whether a hypothesis about the population may or
may not be supported by the results of the sample
2. Confidence intervals: Check whether the hypothesized parameter falls in an interval around
the sample estimate of which we are very confident that it contains the population
parameter, estimate that we are interested in. also tells us which interval we are confident in


Inferential statistics

- Sample
 Sample Mean
 Sample proportion
 Sample estimate
 Sample standard deviation

- Population
 Population parameter
 Population standard deviation
 Unknown


Statistical test

Test Statistic =

- (point estimate –expected value)/SE
- p-value
- H0: reject or retain