Essential Statistics and Experimental Design Techniques for Data Scientists

It’s no secret that data science has become a trendy topic in the last few years. This isn’t a surprise: careers in data science are more relevant than ever as businesses seek to make more data-informed decisions in order to save valuable time, resources, and money.

But data science isn’t just a buzzword, it’s a rigorous field with empirical methodology at its center. Evidence-based testing and analysis drives not only what we understand but how we understand it. Data scientists rely on a wide range of statistical methods, machine learning models and experimental design to derive trends, insights, and ultimately useful information from data. 

A foundation in statistics—including descriptive and inferential statistics, hypothesis testing, regression analysis, and statistical significance—is crucial to creating practical analysis workflows that enable data scientists to reliably derive useful information and predict future outcomes from raw data. And, experimental design frameworks support rigorous testing in a variety of business contexts. 

Foundational Statistical Methods in Data Science

Data scientists leverage these core statistical models and concepts to foundational machine learning models and workflows. These can be used in nearly every function of business, including finance, accounting, marketing, product development, and even product pricing strategy. 

While many individuals can learn basic statistics in a bachelor’s degree program, understanding the advanced statistical reasoning that leverages advanced machine learning algorithms is best learned in a master’s degree program. The curriculum of a master’s in data science will include courses in statistics that are specifically customized to the data science environment, preparing you for future analysis and research projects.

Descriptive and Inferential Statistics 

Descriptive and inferential statistics can be thought of as two sides of the same coin, or rather, the side you can see and the flip side that is almost certainly there. 

• Descriptive statistics refers to what the numbers currently show, or what is demonstrable through the current data. When you’re practicing descriptive statistics, you’re focusing on describing data to reveal what the numbers are saying. 
• Inferential statistics is about finding answers from the data in front of you. It allows you to make projections and predictions using data. 

Data scientists employ both of these methods to analyze data and extract meaningful insights. They tend to use descriptive statistics to summarize and organize raw data and showcase performance. And when using inferential statistics, data scientists use their data to make predictions about big-picture problems or populations. 

In Practice: Descriptive statistics can help delineate current market conditions, while inferential statistical methods can be used to predict how products will perform in larger markets or how pricing may need to change over time. 

Hypothesis Testing

At times, the findings from data are not 100% certain, and therefore require further investigation. When this is the case, hypothesis testing allows data scientists to investigate and confirm (or disprove) their predictions. Alternatively, data scientists can use hypothesis testing to determine how likely a certain outcome is. 

Conducting systemic hypothesis testing requires a few steps:

• Identify key variables
• Establish experimental controls
• Building data analytics or machine learning pipelines
• Design an effective experiment that proves or disproves a hypothesis

Hypothesis testing requires critical reasoning, a thorough understanding of casual relationships, and the resources necessary to executive an experiment. 

In Practice: In an area of business like portfolio management or marketing, hypothesis testing can be an important part of comparing a new strategic direction’s actual results with its intended results, pointing to whether or not a strategy adjustment is needed.

Regression Analysis 

Identifying the relationship between variables in data can be key to:

• Uncovering trends
• Investigating causes of performance 
• Extrapolating nuances
• Deriving useful insights

Regression analysis seeks to identify and describe the relationship between a dependent variable (the variable being tested) and an independent variable (the variable that does not change). 

Regression analysis typically doesn’t define causality with 100% certainty. However, it can help data scientists understand how different data points might be related, which is the first step toward identifying causality. 

In Practice: Regression analysis can be used to investigate whether market upturns or downturns are flukes or something more noteworthy that requires further investigation.

Statistical Significance

Statistical significance uses observed data to identify the likelihood of there being a relationship between two variables. It also helps data scientists determine whether a relationship is due to a particular cause or if it is just a case of random chance. This is a key step toward ruling out the null hypothesis (the assumption that there’s no relationship).

Statistically significant results demonstrate some kind of relationship between two variables, whether that be positive or negative, that is due to a real relationship. Statistically insignificant results do not demonstrate a strong relationship, meaning that the two data points are likely unrelated. 

In Practice: In order to validate any predictions, data scientists need to determine if variables have a relationship that is statistically significant.

How Is Experimental Design Used in Data Science?

Experimental design makes use of these statistical methods in order to construct meaningful, effective experiments that validate data scientists’ projections. Consistent and reliable experimentation can be the differentiator that leads to effective strategies. In competitive markets with abundant data, structured experimentation leads to more informed better decisions and more reliable models. 

In order to develop the statistical expertise and applied analytics skills to conduct experimentation effectively, both practice and education are crucial. An MS in Data Science can provide you with the skills and knowledge you need to deepen your experimentation abilities and make an impact through data. 

A/B Testing for Product Development

A/B testing in product development relies on segmentation to explore how different products perform in market conditions. For example, a business might rotate a product with two different packaging colors at the same time and days over alternating weeks, taking note of any differences in performance. 

Regression analysis can then be used to investigate to what extent customer preferences drive performance and what extent random factors have an impact. 

Regression-based Analysis in Pricing Strategies

Regression-based analysis can be used to hone pricing strategies, balancing revenue generation while avoiding prohibitive pricing. Regression analysis can be used to better understand which consumer behaviors are likely tied to pricing changes and which are a result of the random or external ebb and flow of consumer preference. 

Multivariate Testing in Marketing Campaigns

While A/B testing focuses on changing a single variable, multivariate analysis observes how multiple variable changes can interplay in various segmentations. Some of the variables that can be tested include:

• Imagery
• Headlines
• Calls-to-action (CTAs)
• Page layout
• Videos and gifs vs. static imagery

A multivariate test involves testing all of the possible combinations of the variables you want to test to see what performs the best. While the marketing team are likely the ones proposing which variables to test, the data scientists conduct careful analysis to determine statistical significance, as changing multiple variables can introduce an additional layer of uncertainty. 

Randomized Controlled Trials in Healthcare Settings

In healthcare settings, randomized controlled trials are used to help identify outcomes associated with interventions, medications, lifestyle choices, and more. Researchers use randomized and variable-controlled groups to design tests that can identify what makes the most impact. Then, data scientists analyze those results so that stakeholders can better understand how specific variables can relate to outcomes across broad groups. 

Scalable Pipelines and Data Transparency for Real-World Applications

Data is more available than ever before, and the sheer scale of it can necessitate careful preparation and management. Data pipelines that can scale up to meet evolving needs for data analysis, processing, preparation, and storage are critical in an environment where well-managed data is central to making effective decisions. 

Some ways that advanced software can support scalability and transparency include:

• Automated systems that can collect, process, and transform data for use in other systems while maintaining rigorous statistical analysis.

• AI tools and models can further automate tasks like data cleaning, model selection, and basic coding.

• Explainable AI methods like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) leverage statistical analysis in order to improve transparency and better understand how AI comes to outputs.

About the Online MS in Data Science

The Pace University online Master of Science in Data Science was designed to help students take advantage of professional opportunities in the next generation of quantitative solutions. Our STEM-designated curriculum leverages the Seidenberg School’s decades of experience in online education to explore theoretical and practical approaches to data governance, machine learning, predictive analytics, and more. 

This flexible, 100% online program fits a combination of hands-on experience and asynchronous activities into your schedule, building the expertise you need to guide the future of data-driven organizations. Pace University also offers an on-campus option for the MS in Data Science.