In scientific studies, data collection can fall under two broad categories
The various ways in which data collection experiments can be designed:
Simple random sampling - Selecting a group of n units in such a way that each sample of size n has the equal probability of being selected.
Stratified random sampling - grouping the target population into strata based on a known auxiliary variable and then performing simple random sampling on each of the strata
cluster sampling - this involves selecting groups (based on convenience of study, such as buildings or city blocks) based on simple random sampling and then surveying or measuring all units within the groups.
systematic sampling - this involves selecting every nth value from a finite list as the sample. This technique is economical but can he heavily biased.
There are 2 types of variables in an experimental study - controlled variables (factors) and measured variables (response variables) which are under study
factorial treatment - each of the various factors (m,n,o..) affecting the phenomena are combined with one-another. The resulting combinations (mno..) are studied individually
fractional factorial treatment - as the number of factors increase, factorial treatment becomes impossible to complete. Then, only a few selected combinations are picked for the study forming a fractional factorial treatment.