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Res 342 Week 1 DQ 1

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Week 1 DQ 1: Why do a null and alternative hypothesis have to be mutually
exclusive? In hypothesis testing, why are verbal problem statements converted to
numerical problem statements? Can you accept and reject the null hypothesis? Why
or why not?
The null and alternative hypothesis has been mutually exclusive because if you are
trying to disprove the null hypothesis, this means the hypothesis are not mutually
exclusive. Even though the null and alternative hypothesis possesses mutually similarities the
alternative exclusively disproves and rejects the null by a different predictive result. The reason
for this is due the difference that may have caused the null sampling error. Another thing is that
the logic of traditional hypothesis testing it requires you set up two competing statements or
hypothesis that refer to as the null hypothesis and alternative hypothesis. This is where you
find that these hypothesis are mutually exclusive and exhaustive.
Example below:
H
o
: The finding occurred by chance
H
1
: The finding did not occur by chance
In most cases, the null hypothesis is then assumed to be true unless evidence is found
whereas to the contrary, however, if found that the evidence is just too unlikely given then the
null hypothesis is where we assume the alternative hypothesis is more likely to be correct. So
in this case, the traditional statistics show that a probability of something occurring of less
than.05 (= 5% = 1 chance in 20) is conventionally considered "unlikely". Another thing there
are two types of statistical hypothesis and they include: Null hypothesis – The null hypothesis
denotes by Ho, which is usually the hypoth observations result purely from chance, however,
with alternative hypothesis it denoted by H1, or Ha, that the sample observations are influenced
by some non-random cause.
In hypothesis testing, verbal problem statements are converted to numerical problem
statements because when considering the hypothesis as a trail against the null hypothesis, the
data can be evidence against the mean. Another thing hypothesis testing is undoubtedly one
of the most widely quantitative methodologies in empirical research in social science and it is
also has the starting point of hypothesis testing where it is specifies the hypothesis to be tested
and called the null hypothesis. In other words most of the time in hypothesis testing the null
hypothesis is tested. Accepting or rejecting the null hypothesis I would have to say no. The
reason being is when the data analysis suggests that we reject the null hypothesis. It also
states the sample statistics is sufficiently different from the assumed value of the population
that is unlikely to be explained chance. Also, you never except the null hypothesis. However,
you can either reject or fail to reject it and the data can only provide evidence against the null

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hypothesis, meaning it will not be in favor of it so in this case, you will never accept the null
hypothesis.

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Anonymous
Excellent resource! Really helped me get the gist of things.

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