How To Do a Quantitative Analysis

Quantitative Analysis can be used in the spheres of mathematics and science and hence depending on the area of use, it is defined with the elements to suit that.

According to Sebastian Taylor (2015), in the Corporate Finance Institute, quantitative analysis is the process of collecting and evaluating measurable and verifiable data such as revenues, market share, and wages in order to understand the behavior and performance of a business.

Will Kenton, (2023), in investopedia says quantitative analysis refers to methods used to understand the behavior of financial markets and make more informed investment or trading decisions.

Quantitative analysis in Chemistry refers to  any method used for determining the amount of a chemical in a sample according to the Royal Society of Chemistry, 2024.

Overall it involves the use of mathematical and statistical techniques to analyze data and draw conclusions.



Seeing quantitative analysis uses data to draw conclusions, it is expedient to get the right data in order to draw right conclusions. The wrong data or information can lead to wrong analysis.

In order to be cautious and give correct analysis, the following are ways to do a quantitative analysis.

1. Managing and validating your data which involves familiarizing yourself with the needed software and screening your data: entering the data into a program; and finally, ‘cleaning’ your data. This helps to find out if the data collection was done without any bias.

1. One must understand the variable categories. Distinct data classifications require specific handling, hence it is crucial to differentiate variables based on their relationship (dependent or independent) and their scales of measurement (nominal, ordinal, interval, and ratio).

1. Conduct exploratory data analysis which is used to provide a concise overview of a dataset by utilizing measures such as central tendency (mean, mode, and median), dispersion (range, quartiles, variance, and standard deviation), and distribution.

1. Employ appropriate inferential statistics to help evaluate the capacity to make inferences that go beyond the immediate data.

1. Ensure the selection of the correct statistical test where you have to understand the characteristics of your variables, their measurement scale, the shape of their distribution, and the specific questions you aim to address.

1. Seek statistical significance. This is measured by a ‘p-value’, which assesses the likelihood that your findings are not merely due to chance. A lower p-value indicates a higher level of confidence for researchers that their findings are indeed meaningful.



⦁ It helps to make confident decisions
With quantitative data analysis, you know you have data-driven insights to back up your decisions.

⦁ Reduce costs
Since your conclusions are made by analysis from right data, you are able to spend or use your available resources wisely.


1. Sebastian Taylor, (2015), Quantitative Analysis
2. Will Kenton, (2023) Quantitative Analysis (QA): What It Is and How It’s Used in Finance
3. Sage Publications (2023), Steps in Quantitative Analysis
4. Atlan (2018), Your Guide to Qualitative and Quantitative Data Analysis Methods
5. Hotjar (2023) The ultimate guide to quantitative data analysis
6. Royal Society of Chemistry, (2024) Quantitative Chemistry

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