Introduction: What is Statistical Analysis? Statistical analysis is a collection method to process
large amounts of data and result overall trends. Statistical analysis
is a useful tool to deal with noisy data. It provides ways to objectively report on how unusual an event is
based on historical data. There are two different areas of statistics - descriptive
statistics and inferential statistics; which are related to but
still different from each other.
Descriptive Statistics Descriptive statistics is simply the process of defining characteristics of a statistical measurement. Speaking, descriptive statistics involves a observational study of a population. Charts and graphs are an important role, and some standard measurements such as averages, percentiles, and measures of variation, and the standard deviation. For example in a paper reporting on a study involving human subjects, the table is giving the overall sample size, sample sizes in important subgroups, and demographic or clinical characteristics such as the average age, the proportion of subjects with each gender, and the proportion of subjects with related co-morbidities.
Inferential Statistics Inferential statistics is measuring the
trustworthiness of conclusions about the population parameter based on
its information, this is called
random sample. There are many possible uses of inferential statistics,
for example - political predictions. In order to predict who the winner of a presidential election is: chosen sample from amount of
Americans and asked which way they will be voting. From the answers
given in this situation, statisticians will able to predict what general population will vote for with a high level of
confidence. The keys of inferential statistics are
choosing which members of the general population will be polled and
which questions will be asked.
Software Programs: Open source [source: more info can be found in wiki]
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