# Understanding Statistical Analysis & Its Process # Understanding Statistical Analysis & Its Process

Statistics have proven to be the biggest game-changer in the context of a business in the 21st century, leading to the boom of the new oil “Data”. Data is so powerful that when analyzed, it can change the fortunes of a company. Every company out there is leveraging the data to make its decisions and strategies.

But before going any further let us understand what statistics mean:

Statistics is the Science of Collecting, Presenting, Analyzing, and Interpreting any numerical data.

Data by itself has very little value unless we can understand it, interpret it, and analyze it. A huge amount of data can be analyzed to get valuable insights, but without analysis all of this, data is just a bunch of numbers that wouldn’t make any sense at the first glance.

After analyzing all types of data, it is then categorized into structured data and unstructured data.

Statistical analysis is used to get meaningful information from the raw data by using different techniques such as preprocessing the data, graphical representation, and modeling techniques like (correlation, regression, ANOVA, etc).

Process of statistical analysis

Statistical analysis is a 6 step long process and takes a lot of time to crack important insights. The steps involved in the statistical analysis are as follows :

1. Defining the business objective of the analysis
2. Collection of Data
3. Data Visualization
4. Data Pre-Processing
5. Data Modelling
6. Interpretation of Data

Step 1: Defining the objective of the analysis.

The first step is to understand the reason for the analysis. Here we have to pre-decide, what we want to achieve by doing the analysis? Setting the objective is one of the most important steps of analysis; because this will work as the framework for all the next steps.

Step 2: Collection of the data

Now, this is the most important step in the analysis process. Because here you have to collect the required data from various sources.

There are commonly two methods/sources of data collection-

• Primary Data– Primary data refers to the data that is being freshly collected and is not used in the past. Primary data can be collected via surveys, interviews, and personal observations.
• Secondary Data– Secondary data refers to the pre-existing information, which has already been collected and recorded by some researchers for their own purposes and is openly available to use. Sources of secondary data are the Internet, TV, research papers, etc.

Step 3: Data Visualization

This step is crucial as it will help us understand the non-uniformities of the data in a data set. This will help us sort the data in a manner that will help us fill the gaps and expedite the process of analysis. Various Visualization tools like Tableau, BI, Power BI can be used for Data Visualization.

Step 4: Data Pre-Processing

Data preprocessing is the process of gathering, selecting, and transforming data to analyze data. It is the most time taking process as it accounts for 80% of the time taken for analysis.

Step 5: Data Modelling

After data preprocessing, the data is ready for analysis. We must choose statistical techniques like ANOVA, Regression, or any other technique based on the variables in the data.

Step 6: Interpretation:

We then come to the final step of our analysis which is Interpretation. Data interpretation means implementing various processes through which data can be reviewed to arrive at an informed conclusion.

#### Conclusion

So far we have covered all the important topics related to statistical analysis. And you might have realized that statistical thinking involves the careful study of the cycle, collecting meaningful data to answer a concise research question, and then a detailed analysis of patterns from the data, and finally, drawing conclusions from the data. 