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ALİ RAŞİT BEYLER
A Closer Look at Statistical Analysis and Some Real-Life Applications
When someone mentions the word statistics in some informative results such as in "Statistics
show that 90% of the population are..." or "Results show that only half of those surveyed
are...", he or she is concerned about the results of statistical analysis. That is statistics in
common usage; it is the summarized results derived from initial data. Statistics as a discipline
is concerned much more: it is the painstaking and computation-heavy analysis that precedes
the conclusions and the generation of additional implications after results are made.
(1) Data collection - Data is often collected from what we call samples (the subjects for a
statistical study) and samples are selected properly through what we call sampling methods. It
would be more desirable to use the entire population as the subject for study, but as there are
severe constraints to analyzing the entire population (one of which is sheer size), statisticians
would be satisfied with samples. Samples are usually people, but they can be objects.
Sampling methods are classified as probability sampling methods (where the probability of
each member of the population to participate in the study can be calculated) and nonprobability sampling methods (any sampling method where the probability of each participant
to be chosen in the sample cannot be calculated)
Many think that data collection is as simple as asking the respondents a few questions about a
survey. That is far from actual statistical practice. While this dimension of statistics is not
mentioned in statistics textbooks, it is often mentioned in research textbooks. Apparatus (such
as questionnaires, survey sheets, and interviews) should be as reliable as possible so that they
can extract data from the sample as completely and accurately as possible. Ideally, there
should be no bias in data collection; and all intentional biases should have a rationale behind
it.
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