What you need to know about statistical analysis

Many businesses rely on statistical analysis to organize collected information and predict future trends based on that data. While organizations have many options about what to do with their big data, statistical analysis is a way to examine that data as a whole, as well as break it down into individual samples.

Statistical analysis is the cornerstone of successful business intelligence. We’ve put together the following primer to explain statistical analysis and how it can help your business grow, as well as some of the most popular statistical analysis tools you can use to get started.

What is statistical analysis?

Statistical analysis, or statistics, is the process of collecting and analyzing data to identify patterns and trends, remove bias, and inform decision making. It is an aspect of business intelligence that involves collecting and examining business data and reporting trends.

There are several ways that businesses can use statistical analysis to their advantage, including determining the top performing product lines, identifying poor performing salespeople and getting an idea of ​​how sales performance varies between different regions of the country.

Statistical analytical tools can help with predictive modeling. Rather than showing simple trend predictions that can be influenced by a number of external factors, statistical analysis tools allow businesses to dig deeper to see additional information.

What are the types of statistical analysis?

There are two main types of statistical analysis: descriptive and inferential, also known as modeling.

Descriptive statistics

Descriptive statistics are what organizations use to summarize their data. This type typically involves summary charts, graphs, and tables that depict the data for easier understanding, rather than relying on raw, unorganized data. Among some of the useful data that come from descriptive statistics are the mode, median, and mean, as well as range, variance, and standard deviation. That said, descriptive statistics are not meant to draw conclusions.

Inferential statistics

Inferential statistics provides a way to take the data from a representative sample and use it to draw larger truths. It allows organizations to extrapolate beyond the data set, going a step beyond descriptive statistics. Statistical inference relies heavily on finding as representative a sample as possible from which to draw conclusions about a wider population. Since there will always be uncertainty about extrapolating from a limited set of data to a wider population, statistical inference relies on estimating uncertainty in predictions.

The conclusions of a statistical inference are a statistical proposition. Some common forms of statistical statement include the following.

Estimates: An estimate is a particular value that best approximates some parameter of interest. Confidence Interval: An interval constructed using a data set drawn from a population such that, under repeated sampling of such data sets, such intervals will contain the true parameter value with probability at the stated confidence level defined as ‘ a confidence interval. In other words, the confidence interval is a measure of how well the model predicts the data actually recorded. Credible intervals: A set of values ​​containing, for example, 95% posterior confidence is referred to as a credible interval. This is a way to standardize confidence intervals. When you read about a study with 95% confidence, they are referring to a credible interval.

What are the benefits of statistical analysis?

Is it really worth investing in big data and statistical analysis? The best way to answer that question is to examine the benefits.

Overall, statistics can help business owners identify trends that would otherwise go unnoticed without these methods. The analysis also injects objectivity into decision making. With good statistics, gut decisions are not necessary. [Read related article: Techniques and Tools to Help You Make Business Decisions]

Here are some of the specific business benefits of using statistical analysis.

Cut operating costs.

Statistical analysis can help companies analyze their data and costs more accurately, as well as recognize spending trends. After accurately identifying this information, businesses can extrapolate insights into potential future costs or cost-saving techniques to limit spending and reduce waste.

Suppose you rent a vending machine in your lobby for customers and employees to enjoy drinks and snacks, but you’re not sure if it’s used enough to justify the cost. A statistical analysis can help you quantify the frequency of purchases and the amount of money brought in versus the cost of the machine and the price of keeping it in stock. You may find that the machine is not used much and this is an expense that you can cut out of your budget without negatively affecting your operations.

Conduct market analysis.

Statistical analysis can also help businesses perform accurate market analysis. The data can show where the most sales occur, where the sales have the most value and what marketing is tied to those sales. This allows for improved efficiency in every aspect of sales and marketing.

Consider a business owner with a successful cafe who wants to open a second location. The company can conduct a market analysis to come up with estimates of how much foot traffic there might be in a certain neighborhood, how much disposable income the residents of the area might have, and what tastes the potential customers might have. This information paints a clear picture of the potential location’s viability, allowing the business owner to make an educated decision.

Increase workplace efficiency.

Statistical analysis can improve work efficiency. For example, we know that providing the right tools can get the best work out of employees. Statistical analysis can help employers examine each tool’s effectiveness to focus on those that drive the best performance. Business leaders can also use statistical analysis to identify variables that can help or hurt workplace efficiency, such as whether coworkers eat lunch together or don’t participate in employee networking events.

A particularly useful example of using statistical analysis to analyze workplace effectiveness would be to measure employee output after adopting a new tool or practice. For example, a company can see if adopting workplace virtualization increases worker efficiency.

Improve decision making.

Statistical analysis is the backbone of business intelligence and informed decision making. Descriptive statistics along with A/B testing provide a clear view of which choices resonate with customers or leads. This is especially important for companies that want to expand their offerings or customer lists, as well as for businesses that do not have a steady stable of customers.

Every major business decision should be made only after the idea has been tested and the data reviewed. Website redesign is one example of this. Instead of launching a brand new website, a business should first soft launch a potential new design to select visitors in an A/B test. The organization can use this process to gather insightful information such as the duration of site visits, potential click-throughs and whether sales increased or decreased with the new design. Then they can use statistical analysis to compare these values ​​to the old site and see if the redesign should be fully rolled out, further tweaked, or scrapped altogether.

What is statistical analysis software?

Since not everyone is a mathematical genius who can easily calculate the necessary statistics on the reams of data a company acquires, most organizations use some form of statistical analysis software. This software can provide the specific analysis an organization needs to improve its business.

Such software is able to quickly and easily generate graphs and charts when performing descriptive statistics, while simultaneously performing the more sophisticated calculations required when performing inferential statistics.

The more popular software services for statistical analysis include IBM’s SPSS, SAS, Revolution Analytics’ R, Minitab, Stata, and Tableau, which is now part of Salesforce. You can learn more about the latter provider in our review of the Salesforce CRM.

Software features

The two most important features of statistical software are analysis and presentation. Analysis features include statistical tools that do the heavy lifting when it comes to calculations. Typical analytical functions include standard modeling, confidence intervals, and probability calculations. They provide the core value of statistical software and are the primary reason to invest in such systems in the first place. Despite that, analytical features should not be your primary concern when shopping for statistical analysis software.

Presentation is probably more important. This is what populates charts and graphs. It allows for real-time reporting and all the visual features that make the statistical results accessible. Statistical presentation should always be a major consideration when choosing statistical analysis software.

What is the importance of statistical analysis and business intelligence?

Business intelligence, of which statistical analysis is only one part, is critical to sustainability. A business owner who does not regularly take stock of their business cannot adequately address problems, replicate success or plan for the future. Companies should regularly conduct self-assessments for a better understanding of the organization. In addition to statistical analysis, we recommend doing a Pareto analysis to improve efficiency and decision-making.

Chad Brooks contributed to the writing and reporting of this article.