Non parametric tests
It is a hypothesis test which does not need population distribution. For instance there are numerous hypothesis tests which depend upon assumptions that population must be normally distributed along with the parameters. Main advantages of non- parametric tests are that they do not rely on assumptions, so they can be easily used where population is non-normal. One thing to be kept in mind, that these tests may have few assumptions related to the data. They might not be completely assumption free. These test are also known as distribution free tests.
Types of non-parametric tests Chi-square. Mann- Whitney test Friedman test
Mann-Whitney test This is non-parametric test which compare medians of ordinal of 2 groups. …show more content…
Complex mathematical operations are not required for computation.
Few assumptions about the data.
These tests can be applied where distribution is unknown.
Disadvantages
These tests are used where data is small.
If the data requires numerous observations then ranking method becomes difficult.
Less powerful for data which is normal
Less efficient for data which is normal.
References
Bradley., J. V (1968), Distribution Free Statistical Tests. Prentice Hall: Englewood Cliffs, NJ.
Dallal GE (1988), "PITMAN: A FORTRAN Program for Exact Randomization Tests," Computers and Biomedical Research, 21, 9-15.
Ellis JK Russell RM Makraurer FL and Schaefer EJ (1986), "Increased Risk for Vitamin A Toxicity in Severe Hypertriglyceridemia," Annals of Internal Medicine, 105, 877-879.
Fisher LD and van Belle G (1993), Biostatistics: A Methodology for the Health Sciences. New York: John Wiley & Sons, Inc.
Hollander M and Wolfe DA (1973), Nonparametric Statistical Methods. New York: John Wiley & Sons, Inc.
Lee ET (1992), Statistical Methods for Survival Data Analysis. New York: John Wiley & Sons, Inc.
Lehmann EL (1975), Nonparametrics: Statistical Methods Based on Ranks. San
It is a hypothesis test which does not need population distribution. For instance there are numerous hypothesis tests which depend upon assumptions that population must be normally distributed along with the parameters. Main advantages of non- parametric tests are that they do not rely on assumptions, so they can be easily used where population is non-normal. One thing to be kept in mind, that these tests may have few assumptions related to the data. They might not be completely assumption free. These test are also known as distribution free tests.
Types of non-parametric tests Chi-square. Mann- Whitney test Friedman test
Mann-Whitney test This is non-parametric test which compare medians of ordinal of 2 groups. …show more content…
Complex mathematical operations are not required for computation.
Few assumptions about the data.
These tests can be applied where distribution is unknown.
Disadvantages
These tests are used where data is small.
If the data requires numerous observations then ranking method becomes difficult.
Less powerful for data which is normal
Less efficient for data which is normal.
References
Bradley., J. V (1968), Distribution Free Statistical Tests. Prentice Hall: Englewood Cliffs, NJ.
Dallal GE (1988), "PITMAN: A FORTRAN Program for Exact Randomization Tests," Computers and Biomedical Research, 21, 9-15.
Ellis JK Russell RM Makraurer FL and Schaefer EJ (1986), "Increased Risk for Vitamin A Toxicity in Severe Hypertriglyceridemia," Annals of Internal Medicine, 105, 877-879.
Fisher LD and van Belle G (1993), Biostatistics: A Methodology for the Health Sciences. New York: John Wiley & Sons, Inc.
Hollander M and Wolfe DA (1973), Nonparametric Statistical Methods. New York: John Wiley & Sons, Inc.
Lee ET (1992), Statistical Methods for Survival Data Analysis. New York: John Wiley & Sons, Inc.
Lehmann EL (1975), Nonparametrics: Statistical Methods Based on Ranks. San