What Is Statistical Data Analysis? (With Detailed Guide)

By Indeed Editorial Team

Published July 25, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Statistical analysis applies to every business, industry, and sector because it's the application of relevant data and how it's likely to impact a situation. This process may assist you in forecasting trends, analyzing large amounts of data, and identifying patterns that can help you make better business decisions. If you plan on a career in business, finance, or economics, learning data analysis and statistics can help you advance your career. In this article, we define statistical data analysis, explain how to perform statistics calculations, detail its applications, and review its limitations so you can make valuable insight using available data.

What is statistical data analysis?

Knowing the answer to, “What is statistical data analysis?” may help you understand why this process is crucial to forecasting a company's business performance. Statistical data analysis is the compilation and interpretation of data sets. While this is true in any statistics calculation, the data segments differ in each situation. For instance, a scientist can use statistical weather data to identify patterns and forecast weather conditions. While an insurance business professional may benefit from getting information on motor vehicle patterns instead of obtaining raw data.

Related: Guide to Learning Descriptive vs. Inferential Statistics

How to perform statistical analysis

You can use the following steps to perform data analysis in various situations:

1. Set a goal

The first step in practising data analysis is to set a purpose. Pose a question that you seek to answer through quantifiable data. Sometimes, you can have an existing data set, such as a list of e-mails. In other situations, the analyst can gather that data either digitally or manually. Some examples of data analysis goals include:

  • Determining customer patterns as they relate to weather

  • Assessing the health impacts of a population using housing types

  • Predicting the risk of providing health insurance using demographic information

  • Monitoring sales patterns throughout the calendar year

  • Informing approval ratings of various policy decisions

  • Determining the likelihood that investments can provide returns

Related: Sample in Statistics (Definition and Sampling Techniques)

2. Determine correlations

Any statistical analytics requires a data set with at least two factors. These can range from time to sales revenue. Either you already have the existing data in archives or you can plan to source it elsewhere. There are options that range from surveys to purchasing data from third-party companies.

Once you have the data, the key is to assess how your information relates to the average. This helps you determine what steps to take using the results of that study. For instance, if you see that the company's website traffic increases when there's rainy weather, you can offer discounts on those days to promote sales.

Read more: Understanding Correlation vs. Causation (With Examples)

3. Analyze the data

Because all statistical analyses have unique qualities, it's especially important to determine what approach to take to consider your data. Raw data itself only confers value when you segment it, compare it, and assess those interactions. During the correlation stage, you identify aspects of data that interrelate. Analysis requires you to perform a relevant equation to answer your original question and meet your original goal. There are various ways to perform statistical analyses, including:

  • Random sample mean, median, and mode calculations

  • Random sample variance and standard deviation

  • Sampling quantiles other than median values, including smaller percentiles for niche markets

  • Test-based statistical assessments including chi-square, t-statistics, and f-statistical analyses

  • Ordered statistical analyses, including maximum and minimum values

Related: 10 Skills Business Analysts Need for Workplace Success

4. Make a prediction

When you perform the statistical calculation on the data, usually by using a computer program, you can then make a prediction. While history is the most useful way to assess the future, it remains a likelihood rather than an exact science. Consider your original goal for the data collection and analysis and use your findings to predict future results.

For example, if your original goal is to increase sales, you can gather data related to when the company sold goods, where those sales occurred, and what products the company sold. During the analysis, you may identify correlating values to determine how changes in one element can impact the others. The prediction involves using what you know to make a proposal. For instance, if you notice that t-shirts sell better in summer than fall, you can suggest that dedicating more store space to that item is likely to increase sales.

Related: Hypothesis vs. Prediction: Differences and Characteristics

5. Create an action plan

Once you have the statistical information, the next step is to create an action plan. This is where your unique calculations can offer a return on the time invested. Using your prediction, make a realistic suggestion for testing. This provides real-world data that you can subsequently assess to determine the success levels of your plan. In most situations, businesses perform these tests in small groups to mitigate risk, such as a company introducing a new menu item at select stores.

To communicate your idea to a team of executives or managers, it's important to show your results. Many people learn visually, so creating a folio of materials, including your studies and projections, can help, but work best with visual tools. This includes:

  • Bar chart

  • Pie chart

  • Histogram

  • Distribution table

  • Scatter plot

  • Line graph

Related: What Is a Biased Sample? (Definition and List of Examples)

Practical uses of statistics

Statistics may have an endless set of applications. Here are some of its common applications:

Modelling

Modelling data involves the visual representation of information through the use of charts and graphs. It makes information clear to any reader and can foster engagement. These initiatives are common in public health awareness campaigns. The purpose is to provide accurate data that is extremely accessible.

Related: Inferential Statistics: Definition and Pros and Cons

Marketing

Marketers rely on research and statistical best practices to create surveys. Both can provide data to inform future statistical studies. By identifying bias, determining appropriate market segments, understanding how to incentivize their uses, and learning how to make inquiries clear to consumers, data analysis improves regularly.

Research

Researchers conduct statistical data analyses to assist companies in creating actionable approaches to analysis. Entire software systems and programs exist to facilitate the interpretation of information. The research can range from economic purposes, such as predicting stock trends, to assessing the impact of a specific diet on longevity in certain populations. Research differentiates itself from other analysis types, mainly because it's usually a primary research tool, where its results inform future actions.

Business plans

Businesses use a wide array of statistical analyses, ranging from search engine optimization add-ons in web browsers to separate software that exists for a specific sector, such as health care. Because data analytics is ultimately the pursuit of an actionable set of information, businesses use it to make many types of assessments, including:

  • Establishing strategies for risk

  • Determining optimal advertising methods

  • Deciding how to gain customers affordably

  • Evaluating innovations, such as new products or services

  • Developing a risk model for supply chain and logistics

Related: Parameter vs. Statistics (With Frequently Asked Questions)

Realistic limitations of statistics

Statistical data relies on historical information, even if you gather it specifically for a purpose. It's important then to consider that when you analyze statistical data, the data set already occurred as a unique iteration. This means that there are various flaws inherent to statistics. While these usually are unavoidable, analysts can consider them to mitigate bias and evaluate information accurately. Some limitations include:

Incomplete data

Incomplete data refers to situations where the data exclude a section of the population. For instance, if all study participants on a survey about shopping habits were below the age of 20, it represents an incomplete set of data for the overall population. It can offer a complete data set for those in that age group, but as it excludes other shoppers, it remains incomplete.

Related: How to Interpolate in 5 Steps (With Steps and Examples)

Misleading results

Misleading results in statistics are usually the result of existing unknown bias. Some of these can involve a flaw in the means of contact, such as performing a survey via landline phones. Others can limit the number of subjects to make results seem more significant. For instance, if a study indicates that 80% of dentists agree with an idea, hiding that the study only included 20 dentists, it might mislead the reader into thinking that it refers to the entire dental occupation.

Simpson's paradox

The Simpson's paradox is a term referencing a statistical phenomenon where a trend is present for multiple data sets, yet either disappears or reverses when those groups combine. It can impact the analysis of a prediction if the statistician omits it from consideration, especially because it can be very useful.

For example, consider two salespeople named Jen, who averages sales rates of 88%, and Joe, who averages sales rates of 48%. These two professionals work in different departments and function independently. When the manager combines sales departments, Joe's rate increases to 80% while Jen's rate falls to 70%. By combining the two into one department, an instance of Simpson's paradox occurs by the change in sales values.

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