That statement is False
In case based reasoning, we would try to solve the current cases by using the data from similar past cases.
Because of this, the rules that is uses as a basis to solve the case couldn't be considered as 'flexible' since we're aiming to make a similar approach that implemented in past cases.
Answer:
Are you asking us to compare the two of them?
Explanation:
Answer:
This statement is <u>true</u>.
Explanation:
Inductive argument is based on observation and experimentation, that is why it is important for a sample to be representative.
This critera is valid in scientific research, in which inductive reasoning is used.
The Supreme Court uses the due process clause to incorporate some Bill of Rights protections into the Fourteenth Amendment.
Answer:
Counterbalancing
Explanation:
Order effects is known to be the area of order of treatments in the course of an experiment process. Simply put it this way, putting the treatments in a separate order will cause some changes to the results depending on the order of administration of the treatment just as in the case study.
Order effects could be due to probably anything such as fatigue (from one or two tests), practice (familiar with the testing environment boost performance) or varying Testing Conditions (from the testing environment such as heating, lighting, or ergonomics).
Counterbalancing, comes into play in times of all probable treatments being given in all probable orders can regulate for order effects.
Counterbalancing is a procedure used to solve order effects when employing a repetitive process design. With counterbalancing, the participant sample in our case study is grouped in fours using a within-subjects design with four levels to his independent variable. Conner has ¼ of the participants complete each level in ABCD order, a ¼ of participants in BCDA order, a ¼ of participants in CDAB order, and a ¼ of participants in DABC order.
The varying order effects would be compensated for by this procedure.
For within-subjects design, two major counterbalancing techniques would be used involving subject-by-subject counterbalancing, which regulates progressive error for every subject, and across-subjects counterbalancing that evenly spread progressive error over all subjects.
