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Two Sample Tests
In some situation the researchers may be taking statistical decisions based on two samples. Each of the two samples may represent a particular under consideration. For example one may be interested to study the effectiveness of teaching methods like unstructured teaching and structured teaching. Suppose college have changed their method of teaching from unstructured teaching to structured teaching recently. Then the researcher has to take a random sample of students of a particular programme from different colleges and collect their performances before introducing the structured methods of teaching and after introducing the structured method of teaching. The respective observation will form two samples .
There are many more examples which are as listed below:
a.Number of defects found in the daily lots of items produced by a set of similar machines before and after installing a costly automatic inspection systems in them.
b. Monthly sales revenues of a product in different regions before launching an advertisement campaign and after launching the advertisement campaign.
c.Number of daily transaction in a bank before and after introducing computers.
d. Number of outpatients treated in a government hospital before and after providing modern infrastructure.
There are various non parametric tests but we will discuss only a few of the better known and more widely used ones.
a.The sign test
1. One samples test
2.Two sample sign test ( sing test for paired data)
3.A rank sum test ( Mann Whitney u test)
4. The one sample runs test
5.Rank correlation ( described in chapter 10 )
Data Management As your research project progresses you will quickly gather more and more data. These need to be managed in such a way that they can be accesses and
Books a. One author : Authors last name first name title of books place of publication: publisher copyright data page numbers used. b. Two Author : First author la
Small Sample The central limit theorem does not work well with small samples even if the population in non Gaussian. So we cannot rely on the central limit theorem
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