Statistical data analysis techniques

Written by stephen byron cooper Google
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Statistical data analysis techniques
Statistical analysis starts with source data. (John Foxx/Stockbyte/Getty Images)

Statistical data analysis aims to find patterns in raw data to explain an event or condition; for example, predictions of future events can be derived from historical data and signs of systemic errors can be highlighted in output data. Basic statistical analysis aims to find averages and the deviations from those observations, trends and patterns. These methods form the basis of scientific research in fields such as economics, demographics and ecology. The analysis of processing statistics helps business managers improve company profitability.

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Sampling is a specialist field within the subject of statistical data analysis. This covers the methods used to gather the source data for analysis. Sampling techniques vary from subject to subject. For example, the collection of items from a production line will either use a random function or select one item after a set number of products have been produced. Medical selection of test subjects is more focused on getting a broad range of people that represent the average person who is likely to be subjected to the condition under study. Issues of “bias” are a key concern in sampling. The subjects of the study may choose to skew the results, or the researcher may load the conditions of the study in favour of the results he hopes to find.

Standard deviation

The central statistical analysis technique is standard deviation. This plots the incidences of a condition in the population under study and expects to arrive at a standard bell curve. The methods that contribute towards the standard deviation technique describe basic factors in the source data. These include range, which notes the lowest figure and the highest figure in the data; mean, which is the total of all values in the data divided by the number of the values; and median, which is the central number if all values are ordered by magnitude. The standard deviation aims to find the central wedge of data which represents the majority (95 percent) of the results. This technique focuses on one set of data and analyses the relationship of each data point to the others in the population.

Trend analysis

Financial planners and investment analysts like trend analysis. They seek to predict the future profitability of a company by examining historical data and trying to find a pattern pointing to an upward or downward trend.


Trend analysis and standard deviation examine one group of data. Many medical studies seek to define the performance of one group with that of another group or groups and the difference in the results of the groups reveals an outcome for the action under study. For example, a study seeking to test whether smoking causes cancer will compare the health results of a group of smokers against those of a group of average citizens, both smokers and non-smokers. This average group is called the control group. If more people in the smoker’s group get cancer than in the control group, it could be said that smoking causes cancer.

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