Machine Learning

There are two ways in which hypotheses can be tested:
a. Correlational research
This is also known as cross-sectional research This involves observing the natural pattern or occurrence to test Original occurrences are not manipulated
b. Experimental research
We select the variables of interest Then we manipulate some aspect of the environment Observe the effect on the selected variable

There are two types in variation explained as follows:
Systematic variation:
Introduced by experimenter
The participants are tested under different conditions and the difference in condition is introduced by experimenter
For Example to test use of woolen clothes w.r.t. temperature, we can test a group of 20 people, in both hot and cold climate. Thus, the difference introduced here is in terms of temperature only.
Unsystematic variation:
Introduced by random factors that exist between the experimental conditions.
For Example To test use of woolen clothes w.r.t. temperature, we can test a group of 20 people. Of the selected set some might behave differently than expected due to factors like illness and so on.

Same measure is measured under different conditions on same set participants.
The difference in two conditions can be caused by the following:
The manipulation/changes that was carried out on the participants
Factors that might affect the way in which a participant performs from one time to the next

Same measure is measured under different conditions on different set of participants. The differences between the two conditions can be caused by the following: The manipulation/changes that were carried out on the participants Difference in nature or characteristics of participants in each case

By using randomization we can ensure that any variation introduced, is due to changes in the conditions/variables introduced rather than any other unexpected changes during the process. Thus, it helps in removing other sources of systematic variation.