Research and Statistical Methods for Business

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Running Title: RESEARCH AND STATISTICAL METHODS FOR BUSINESS
RESEARCH AND STATISTICAL METHODS FOR BUSINESS
STUDENT’S NAME:
INSTITUTION NAME:

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RESEARCH AND STATISTICAL METHODS FOR BUSINESS
1. Is the sample size necessary?
Sample size is the observations or the number of participants included in the study. The
sample size is usually indicated by letter n has two major importance; for the precision of the
estimates and it gives the power for drawing a conclusion.
Choosing to work with data of 15,000 employees instead of 69,000 employees can give
some sense of precision when dealing with such data. 15000 sample size is able to give the
needed information of all the employees because no matter how accurate the data collection
might be, the study results can have some margin of error. This is because not everyone was
interviewed. There could be no absolute precision in all the variables collected about the
employees, how these variables affect each other, which is why there is an error considered. This
error is called sampling error and it influences the description on the factors affecting employees
in their various areas of work, but a point will reach, known as diminishing returns, where the
increase in sample size will not have any effect on the sampling error. In most cases sampling
error is an unavoidable. Large sample size has small sampling error compared to small sample
size because when clear picture of the data is needed, more and more observations needed to be
done for comparison.
Sample size also helps in building the confidence of the researcher. At one point the
researcher might need to build his confident interval on the estimates of the data. The confidence
of the researcher can be increased with the increase in sample size. When the interval between
the confident estimates is smaller then the researcher is more confident with the data.
2. What is the current method of sampling? What are the advantages and
disadvantages of the current sampling method?
Random sampling
Random sampling, which sometimes is known as method of chances, is a sampling where
each item in the population has an even chance and likelihood of being selected. This method of
sampling entirely depends on the probability or a chance. It is fair in terms of establishing the
size of the sample.
Advantages of random sampling.
There is less risk of carrying an error with random sampling: Since random sampling
gives an equal chance to every item in the population to be selected, the sampling size will be

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RESEARCH AND STATISTICAL METHODS FOR BUSINESS
reflecting the entire population giving data the ability to give clear insights into some specific
matters.
It is easy to form a sample groups: Random sampling takes only a few items from the
population makes it easy to form sampling group from large population. This makes it very
simple to use or apply.
Random sampling requires less knowledge to complete the research: Random
sampling can be used even if the researcher do not have proper knowledge of the population in
question. Once the researcher has developed the question, he doesn’t have to have a vast
knowledge on the population in question.
Random sampling is the simplest form of collecting data: Once the researcher has the
basic skills on the observation and recording, he will require no basic skills in out of the base of
population or the item being investigated. This reduces the errors associated with classifying the
population under study according to some variability.
Findings of the random sampling is able to reflect the whole population: Random
sampling gives every item the probability of being chosen, this can reduce the biasness and
represents the entire population without the researcher worrying about some part of the
population left out.
Several randomness types can be used to reduce the bias of the researcher: the two
approaches common with random sampling to reduce biasness are lottery method and random
numbers methods. Lottery method which involves drawing a population group and see the ones
included and the ones who will not be included can be used with random sampling. Research
numbers can also be applied where random numbers are given to some specific individuals and
then collect these random numbers randomly.
Disadvantages of Random Sampling.
Random sampling does not take into consideration the additional knowledge: Although
random sampling removes the unconscious bias, sometimes dealing with intentional bias can be
very difficult. The researcher can choose a specific group to carry out sampling on, a population
he knows has specific characters he needs. This do not consider any additional knowledge which
can be gathered from the population.
Random sampling is complex and can consume a lot of time: For the proper collection
of data, each individual selected must be interviewed. The people from the same group can have