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Gathering and Organizing Statistical Data


Statistical data is gathered in order to facilitate management decision making. Once data is collected it must be organized and presented in a meaningful and useful fashion. Data presented in a useful way can be used to draw conclusions about the population being analyzed. Statistical analysis is usually performed based on a sample of a larger population. If the sample has no imposed influences and is large enough the results will yield a good picture of the overall population. Following are four terms utilized in statistical analysis:


  1. Descriptive Statistics – This term refers to the process by which large amounts of data are organized and summarized for analysis purposes. Statistical techniques are used to organize the data in meaningful form. Different types of charts can then be used to present the organized data for decision support purposes.
  2. Inferential Statistics – This term – also referred to as statistical inference or inductive statistics – is a technique used to learn something about a population based on a sample. For example if an auto repair shop wanted to find out what percentage of its customers drink coffee they could pose the question to a random sample of customers and draw an inference from the results of that survey.
  3. Population – This term refers to the overall group being analyzed. It can refer to people or things (such as a certain type of car part). It may be the people that were exposed to a specific ad campaign or service offering. It can refer to the vehicles that had a certain type of brake pad installed within a given time period. Or it can refer to the customers or vehicles that visited the shop within a specified time period. The key is that a clearly defined group is specified.
  4. Sample – This is a subset of the population. In statistics this is often referred to as a ‘random sample’. This terminology specifies that no other factors should influence the selection other than the definition of the population. One of the greatest dangers to the validity of conclusions drawn from statistical samples is incorporation of influences that slant the results.
  5. Sample Size – The size of the sample should adequately represent the population being analyzed. The top polling organizations such as Gallup or Rasmussen generally use a sample in the hundreds or low thousands when analyzing people’s perspective on important social, political or economic topics. The population they’re analyzing usually numbers in the millions. For analytical purposes in a small business environment a sample of 5 would be considered a small sample while a sample of 25 would be considered a large sample. So a good deal of business decisions can be based on samples that are not overwhelmingly large.

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