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Chi-Square Test Overview & Key Terms

The document defines important terms related to parametric and non-parametric tests, including hypothesis testing. It states that parametric tests assume data follows a specific distribution, while non-parametric tests do not. The null hypothesis proposes that there is no association between variables, while the alternative hypothesis proposes that variables are related.

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Hebsiba Ponnayyan
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73 views27 pages

Chi-Square Test Overview & Key Terms

The document defines important terms related to parametric and non-parametric tests, including hypothesis testing. It states that parametric tests assume data follows a specific distribution, while non-parametric tests do not. The null hypothesis proposes that there is no association between variables, while the alternative hypothesis proposes that variables are related.

Uploaded by

Hebsiba Ponnayyan
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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CHI-SQUARE TEST

MS.HEBSIBA P
ASSOCIATE PROFESSOR
DEPT.OF MEDICAL SURGICAL NURSING, SGNC
IMPORTANT TERMS
• 1) PARAMETRIC TEST: The test in which, the
population constants like mean, std deviation, std error,
correlation coefficient, proportion etc. and data tend to
follow one assumed or established distribution such as
normal, binomial, poisson etc.
• 2) NON PARAMETRIC TEST: the test in which no
constant of a population is used. Data do not follow any
specific distribution and no assumption are made in these
tests. E.g. to classify good, better and best we just
allocate arbitrary numbers or marks to each category.
• 3) HYPOTHESIS: It is a definite statement about the
population parameters.
• 4) NULL HYPOTHESIS: (H0) states that no
association exists between the two cross-tabulated
variables in the population, and therefore the variables
are statistically independent. E.g. if we want to
compare 2 methods method A and method B for its
superiority, and if the assumption is that both methods
are equally good, then this assumption is called as
NULL HYPOTHESIS.
• 5) ALTERNATIVE HYPOTHESIS: (H1) proposes
that the two variables are related in the population. If
we assume that from 2 methods, method A is superior
than method B, then this assumption is called as
ALTERNATIVE HYPOTHESIS.

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