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Running head; NULL HYPOTHESIS AND TESTING 1
Null Hypothesis and Testing
Student’s Name
Institution of Affiliation
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NULL HYPOTHESIS AND TESTING 2
Null Hypothesis and Testing
The null hypothesis is an important aspect in hypothesis testing which usually serves as a
pinpointing statement on the quantitative value which is unknown on the population of interest.
The null hypothesis is denoted by H0 which is followed by a colon, a parameter value then equal
sign and finally the numerical value that has been selected by the researcher (H0: r=0.67).
There are some rules which guide the null hypothesis making it important and useful for
research inference. Firstly, the null hypothesis must lie somewhere on the possible values
continuum in correspondence to the parameters and secondly they cannot be fixed on the upper
or lower limit. Therefore, if the null hypothesis states the expected relationship and they lie on
the possible value continuum, they are useful for the researcher to test the population of interest.
Hence, the null hypothesis usually approximates the phenomenon’s description which works as a
framework on the study’s inference reporting. At the end of the hypothesis testing, the researcher
must make their decision by taking their position through stating whether they reject or accepting
the null hypothesis or stating whether the research was statistically significant (Huck, 2012).
It is important for a researcher to evaluate the results for one to ensure that the findings
are not by chance. Findings from hypothesis testing are the basis of the decision making; hence,
they speak entirely for study. Accepting the null hypothesis gives the researcher a go-ahead sign
or pushing them back to the previous stages focused on the evidence assessment. Hence, the null
hypothesis is the key inference to the research result which guides in the decision making of the
study (Huck, 2012). Therefore, is important to test the null hypothesis efficiently since failing to
do so will affect the entire study.
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NULL HYPOTHESIS AND TESTING 3
References
Huck, S. W. (2012). Two-way analyses of variance. Reading Statistics and Research.(6th ed.).
Boston, MA: Pearson, 276-311.

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Running head; NULL HYPOTHESIS AND TESTING Null Hypothesis and Testing Student’s Name Institution of Affiliation 1 NULL HYPOTHESIS AND TESTING 2 Null Hypothesis and Testing The null hypothesis is an important aspect in hypothesis testing which usually serves as a pinpointing statement on the quantitative value which is unknown on the population of interest. The null hypothesis is denoted by H0 which is followed by a colon, a parameter value then equal sign and finally the numerical value that has been selected by the researcher (H0: r=0.67). There are some rules which guide the null hypothesis making it important and useful for research inference. Firstly, the null hypothesis must lie somewhere on the possible values continuum in correspondence to the parameters and secondly they cannot be fixed on the upper or lower limit. Therefore, if the null hypothesis states the expected relationship and they lie on the possible value continuum, they are useful for the researcher to test the population of interest. Hence, the null hypothesis usually approximates the phenomenon’s description which works as a framework on the study’s inference reporting. At the end of the hypothesis testing, the researcher must make their decision by taking their position through stating whether they reject or accepting the null hypothesis or stating whether the research was statistically significant (Huck, 2012). It is important for a researcher to evaluate the results for one to ensure that the findings are not by chance. Findings from hypothesis testing are the basis of the decision making; hence, they speak entirely for study. Accepting the null hypothesis gives the researcher a go-ahead sign or pushing them back to the previous stages focused on the evidence assessment. Hence, the null hypothesis is the key inference to the research result which guides in the decision making of the study (Huck, 2012). Therefore, is important to test the null hypothesis efficiently since failing to do so will affect the entire study. NULL HYPOTHESIS AND TESTING 3 References Huck, S. W. (2012). Two-way analyses of variance. Reading Statistics and Research.(6th ed.). Boston, MA: Pearson, 276-311. Name: Description: ...
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