Data is no longer a scarcity with this being the information age. It is more overpowering than anything. The key is to sift through the overwhelming volume of data available to organizations and businesses and correctly interpret its implications. In order to be able to sort through all this information, you need the right statistical data analysis tools.


The current obsession over “big data” analysts have produced a lot of fancy tools and techniques available to large organizations. T


Start your data analysis efforts with the following fundamentals and learn to avoid their disadvantages below.




The arithmetic mean, or the “average” is the sum of a list of numbers divided by the number of items on the list. Determine the overall trend of a data set by using the mean. It is also useful in providing a rapid snapshot of your data. It is also quick and easy to calculate.


Disadvantage: The mean, when looked at alone is a dangerous tool. The mean is closely related to the mode and the median (two other measurements near the average). The mean doesn’t provide accuracy when a data is set with a high number of outliers or skewed distribution.


Standard Deviation


The Greek letter sigma often represents the standard deviation and it is the measure of a spread of data around the mean. When the standard deviation is high, it signifies that data is spread more widely from the mean. A low standard deviation signals that more data align with the mean. The standard deviation is useful for determining the dispersion of data points.


Disadvantage: The standard deviation, like the mean, is deceptive. For example, if the data have a very strange pattern like a non-normal curve, then the standard deviation won’t give you all the information you need.



The relationship between dependent and explanatory variables is what regression models. Usually, they are charted on a scatterplot. The regression line also designated whether those relationships are strong or weak. High school and college statistics courses teach regression. The applications included are for science or business in determining trends over time.


Disadvantage: Regression is usually not very nuanced. The outliers on a scatterplot typically matter significantly. For instance, an outlying data point may represent the input from your most critical supplier or your highest selling product.