Hypothesis testing and estimation are used to reach conclusions
about a population by examining a sample of that population.
Hypothesis testing is widely used in medicine, dentistry, health
care, biology and other fields as a means to draw conclusions about
the nature of populations.
Hypothesis testing is to provide information in helping to make
decisions. The administrative decision usually depends a test
between two hypotheses. Decisions are based on the outcome.
Hypothesis testing is to provide information in helping to make
decisions. The administrative decision usually depends on the null
hypothesis. If the null hypothesis is rejected, usually the
administrative decision will follow the alternative
hypothesis.
Hypothesis testing is a form of inferential statistics that
allows us to draw conclusions about an entire population based on a
representative sample.
A hypothesis test assesses your sample statistic and factors in
an estimate of the sample error to determine which hypothesis the
data support.
When you can reject the null hypothesis, the results are
statistically significant, and your data support the theory that an
effect exists at the population level.
The null hypothesis is one of two mutually exclusive theories
about the properties of the population in hypothesis testing.
Typically, the null hypothesis states that there is no effect
(i.e., the effect size equals zero). The null is often signified by
H0.
In all hypothesis testing, the researchers are testing an
effect of some sort. The effect can be the effectiveness of a new
vaccination, the durability of a new product, the proportion of
defect in a manufacturing process, and so on. There is some benefit
or difference that the researchers hope to identify.
However, it’s possible that there is no effect or no difference
between the experimental groups. In statistics, we call this lack
of an effect the null hypothesis. Therefore, if you can reject the
null, you can favor the alternative hypothesis, which states that
the effect exists (doesn’t equal zero) at the population
level.
The alternative hypothesis is the other theory about the
properties of the population in hypothesis testing. Typically, the
alternative hypothesis states that a population parameter does not
equal the null hypothesis value. In other words, there is a
non-zero effect. If your sample contains sufficient evidence, you
can reject the null and favor the alternative hypothesis.
Point Estimation deals with the method of estimating an unknown
parameter of a population based on Random Samples from the same
population. The assumption here is that the parameter to be
estimated is a constant with one value and the sample
Statistic computed from the sample is estimating that value
exactly. In the parameter space, it is represented as a point.
Hence the name point estimation. Maximum Likelihood is such a
method.
When you are given some sample and we generally know the
probability distribution form of the population this sample is
given from. By using these information we try to find out its
parameters using some suitable statistic. For better results, we
may want the statistics to be sufficient, consistent, efficient,
complete, etc.
Thus, This estimated parameter can be attain any value in the
parameter space,whereas in testing of hypothesis, you are given a
null hypothesis that the parameter has a certain value and you have
to test if the hypothesis is true or not.