A brief guide measures of statistical data analysis

The area of statistics is the art of discovering from data. Statistical knowledge helps you use the precise techniques to manage the data, apply the accurate investigations, and efficiently display the events. Statistics is a significant method to investigate how we make explorations in science, make choices based on data, and make forecasts. Statistics enables you to understand a topic much more intensely.

In the information age, data is not scarce – it’s uncontrollable. The solution is to examine the remarkable amount of data accessible to companies and businesses and accurately interpret its associations. But in order to perform and understand the underlying pattern of data we need statistical tools and techniques to deep down within.

With the popular fascination over big data, interpreters have created a lot of excellent means and methods accessible to huge companies. Though, there are a few basic data analysis tools that the largest corporations aren’t using to their advantage.

This blog will provide a brief about all the major commonly used technique to explore the data to understand the underlying pattern and summarizing data.

While analysts apply analytical procedures accurately, they manage to generate reliable outcomes. Statistical analyses consider risk and inaccuracy in the results. Statisticians assure that all perspectives of a study follow the suitable techniques to provide reliable results. 

These techniques incorporate:

  • Generating reliable data.

  • Examining the data judiciously.

  • Extracting logical inferences.

1. MEAN:

Mean (arithmetic average) is the sum of all the records divided by the number of records. The mean may get affected by extreme variables or outliers in the data to be examined. Mean helps discover the overall trend of data by providing a speedy picture of our data. Another benefit of the average is that it’s pretty simple and quick to measure. Mean alone can be really dangerous for any prediction as it can be confused with the median or mode for some sample. The mean can easily inaccurate in case of outliers and can show skewness in the data. The formula for the mean :

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 x = each observation 

 n = number of observations.

The mode: the mode is a measure of central tendency that is different from the mean but somewhat like the median because it is not actually calculated by the ordinary processes of arithmetic. The mode is the value that is represented most often in the data available.


The standard deviation estimates the average range around the mean and hence it provides a knowledge of the ‘standard’ distance of the average. The standard deviation is the square root of the variance. They are determined by 

  1. Estimating the deviation of each value of the mean.

  2. Squaring each one

  3. Adding the squared deviations;

  4. Divide by the number of items -1.

This provides the variance:

The formula below provides the standard deviation:


 x: sample mean

 xi : ith element 

 n: number of elements 

3. SKEWNESS: curves representing the data points in the data set may either be synmmeterical or skewed. Symmeterical curves such as 


Statisticians usually use a specimen to extract conclusions regarding the population from the sample. They use probability distribution that is very powerful in the area of statistics i.e.  the sampling distribution. It is a theoretical distribution. Therefore, the distribution of sample statistics is called sampling distribution. To determine the accuracy of the data to examine its very important to take sufficiently‌‌ ‌‌large samples to perform hypothesis testing and predict the results of the future decisions. It is the most important parameter to keep in mind to perform any type of statistical analysis. 


The modality is estimated by the number of peaks a distribution contains. Some distribution might have two or more peaks. 

Unimodel means that distribution has one peak i.e is also means the frequency of score is the most occurring score that is clustered at the top. In bimodal, it has two scores that are frequently occurring. In multimodal, three or more scores are frequently occuring in the sample.

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