What is statistical analysis in research

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Statistical Analysis and the Scientific Method
When Statistics Lie
What is a statistical analysis
How to perform a functional statistical analysis
Advantages of Statistical Analysis
Uses of statistics in data analysis

Statistics Definitions > What is Statistical Analysis?

A pie chart is one way to display data.Statistical analysis is the science of collecting data and uncovering patterns and trends. It’s really just another way of saying “statistics.” After collecting data you can analyze it to:

Summarize the data. For example, make a pie chart.
Find key measures of location. For example, the mean tells you what the average (or “middling”) number is in a set of data.
Calculate measures of spread: these tell you if your data is tightly clustered or more spread out. The standard deviation is one of the more commonly used measures of spread; it tells you how spread out your data is about the mean.
Make future predictions based on past behavior. This is especially useful in retail, manufacturing, banking, sports or for any organization where knowing future trends would be a benefit.
Test an experiment’s hypothesis. Collecting data from an experiment only tells a story when you analyze the data. This part of statistical analysis is more formally called “Hypothesis Testing,” where the null hypothesis (the commonly accepted theory) is either proved or disproved.

Statistical Analysis and the Scientific Method

Statistical analysis is used extensively in science, from physics to the social sciences. As well as testing hypotheses, statistics can provide an approximation for an unknown that is difficult or impossible to measure. For example, the field of quantum field theory, while providing success in the theoretical side of things, has proved challenging for empirical experimentation and measurement. Some social science topics, like the study of consciousness or choice, are practically impossible to measure; statistical analysis can shed light on what would be the most likely or the least likely scenario.

When Statistics Lie

While statistics can sound like a solid base to draw conclusions and present “facts,” be wary of the pitfalls of statistical analysis. They include deliberate and accidental manipulation of results. However, sometimes statistics are just plain wrong. A famous example of “plain wrong” statistics is Simpson’s Paradox, which shows us that even the best statistics can be completely useless. In a classic case of Simpson’s, averages from University of Berkeley admissions (correctly) showed their average admission rate was higher for women than men, when in fact it was the other way around. For a more detailed explanation of that brain bender, see Simpson’s Paradox.

For some examples of deliberate (or plain dumb) manipulation of statistics, see:

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Having a set of interpretations about the results of an investigation is a great tool that statistical analysis provides us with, it also allows us to develop statistical models, and design surveys and research studies, especially when we are talking about large volumes of data.

What is a statistical analysis

We know data analysis involves an in-depth review of each part of a whole to understand its structure and interpret its operation. Statistics, on the other hand, is the science that uses probabilities as a basis to influence the possible outcomes of situations that are determined by numerical data when collecting, interpreting and determining their validity.

Therefore, statistical analysis occurs when we collect and interpret data with the intention of identifying patterns and trends; this means that while it’s a type of data analysis by itself, it is approached with an interpretive angle that is useful for making specific decisions and understanding a company’s potential customers, their behavior and experiences.

“Today, statistics is a tool that cannot be lacking in the analysis of data from an investigation, because from the conception of the idea of what is going to be investigated, through the definition of objectives, hypotheses, variables, collection of the data, organization, review, classification, tabulation and production of the results for their analysis it is important to know how to give an appropriate use to the different measures and statistical models for the analysis.

When it is accomplished, the results obtained represent a true contribution to solving the problems inherent to the field where the activities inherent to the different investigations are carried out. ” —Profr. Gerardo Bauce

With a statistical analysis, we can answer questions such as the following:

Who are our clients?
How much does a client pay in a visit?
What is the age of our clients?
How can we categorize our types of clients?
What types of experiences do our clients enjoy?

Identifying patterns of behavior or different trends in a set of data helps companies observe and record the buying behavior of their customers, both to improve their products or services and to facilitate an updated and improved shopping experience, obtaining satisfied customers and great brand awareness as a result.

How to perform a functional statistical analysis

In order to perform statistical analysis, we need to collect and review the data samples available in the results of the study to be analyzed.

Although there is no single way to carry out an interpretive analysis, there are practices that can be replicated in any study if they are carried out in the appropriate way to the information provided. These tips will allow us to carry out a useful statistical analysis.

Give a clear and realistic description of the data we have.
Analyze how the data is related to the study subjects.
Design a model that considers and describes the relationship between the data and the study subjects.
Evaluate the model to determine its validity.
Consider scenarios and tests using predictive analytics.

Advantages of Statistical Analysis

Statistical analysis provides numerous benefits to make the best use of the vast amount of data available, such as assisting in market research, product development, mapping company growth rate, and improving company efficiency.

By making a more straightforward interpretation of a set of data, from qualitative data to quantitative data, through its analysis and categorization, we will be able to manipulate data and adjust situations in specific contexts with appropriate decision-making. In general, the statistics will help with:

Identifying trends that go unnoticed.
injecting objectivity into decision-making.
No need to make gut decisions.
Reducing operating costs.
Performing market analysis.

The statistics show where the most sales are occurring, where the sales have the most value, and what marketing is attached to those sales. This allows you to improve efficiency in all aspects of sales and marketing.

Similarly, analytics can help work efficiency. In many cases, providing the right tools will get the best work out of your employees.

Uses of statistics in data analysis

When we have a broader notion of the trends around our market, we are ahead since we can use statistical analysis to predict future behaviors resulting from the implementation of appropriate strategies for risk management.

In addition, we will have specific data on consumer behavior to identify their preferences, the products or services that are most and least attractive to them, and how to approach them to make a purchase.

There’s always new trends and behaviors among clients or even employees that we need to constantly review, for this reason, to carry out more complex data with specific tools and functions to design a statistical analysis, we recommend using survey software such as QuestionPro.

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