Statistical analysis of big data is a powerful tool that organizations use to make sense of data and guide their decision-making.

As the tools and techniques around big data proliferate, there is no shortage of methods that have been around for a while now, but have not lost their touch of precision – statistical analysis methods.

Data accumulates and people wonder what can be done with it. In the information age, there is no shortage of data; the data is overwhelming. The key is to sift through the overwhelming volume of data available to businesses and organizations, thus correctly interpreting its implications. Maybe a few methods of statistical analysis can help find some golden nuggets buried in all this noise.

There are obviously thousands of big data tools out there, all promising to save your time and money, and also uncover unprecedented business insights. While all of this may be true, navigating the maze of Big Data tools can be quite overwhelming and tricky. We suggest that you start your data analysis efforts with a handful of basic, yet effective, statistical analysis methods for big data before moving on to more sophisticated techniques.

Here are five fundamental statistical analysis methods you can start with, as well as pitfalls to watch out for.


1. Average

More commonly known as the mean, the arithmetic mean is the sum of a list of numbers divided by the number of elements in the list. By using the average method, you can determine the general trend of a data set or get a quick snapshot of your data. This method offers the advantage of a simple and fast calculation.


If used alone, the mean is a dangerous tool, and in some datasets it is also closely related to the mode and the median. Remember that in a dataset with a skewed distribution or a high number of outliers, the mean simply doesn’t provide the kind of precision needed for a nuanced decision.

2. Standard deviation

It is a measure of the dispersion of the data around the mean. While a high standard deviation means that the data deviates widely from the mean, a low deviation signals that most of the data aligns with the mean. This method of statistical analysis is useful for quickly determining the spread of data points.


Similar to the mean, the standard deviation is also misleading if taken alone. For example, if the data has many outliers or a strange pattern such as a nonnormal curve, the standard deviation will not give you all the information you need.

3. Regression

The relationship between the dependent and explanatory variables is modeled using the regression method. The regression line helps determine whether these relationships are strong or weak, as well as trends over time.


The regression is not very nuanced, and outliers on a scatterplot (and their reasons) matter significantly. For example, a distant data point may represent your best-selling product. The nature of the regression line is such that it tempts you to ignore these outliers.

4. Determination of sample size

A sample does the job just as well when the data set is large and you don’t want to collect information from each item in the data set. The trick is to determine the right size for the sample to be accurate.


When analyzing a new, untested variable in a dataset, you have to rely on certain assumptions, which may be completely inaccurate. If such an error affects your sample size determination, it can affect the rest of your statistical data analysis.

5. Hypothesis testing

This method involves testing whether a certain premise is actually true for your dataset. The results of this test are statistically significant if the results could not have occurred by chance.


To be rigorous, watch out for the placebo effect, as well as the Hawthorne effect.

These statistical analysis methods add a lot of information to your decision-making portfolio. Skipping these methods for many other sophisticated tools and techniques will not be wise on your part.