- Random sampling - The goal of this is representativeness, we aim to get an equal probability of selection to every member of the population. There are a few methods:
- Simple random sampling - A sample so that every item or person in a population has the same chance of being included.
- Systematic random sampling - Items or individuals are arranged in some sort of order. A random starting point is selected and then every nth member is selected. Alphabetic order for example.
- Stratified random sampling - A population is divided into sub groups (strata) and a sample is selected from each strata.
- Cluster sampling - A population is divided up into primary units and then samples are selected from the primary units.
- Non-probability sampling - Inclusion in the sample is based on the judgement of the person selecting the sample. (Eeek!)
- Sampling Distribution - This is the theoretical distribution of a statistic for all possible samples of a certain sample size, N. It's a device to link the samples characteristics to the population.
- If repeated sample sizes of size N are drawn from a normal population with a mean of mew and a standard deviation, σ, then the sampling distribution of sample means will be normal with a mean of mew and a standard deviation of σ / SqrRoot(N).
- The 'Central Limit Theorem' states that if repeated samples of size N are drawn from a population, as N becomes large the sampling distribution or sample means will approach normality.
- Or, in easier terms: Large samples are more reliable!
The next post will go further into the concept of confidence intervals and we will introduce such things as error margins. Stay tuned, thanks guys!