Statistical Methods That Unlock Powerful Insights

Data is critical in every industry to help organization leaders make intelligent decisions. However, your data is only as valuable as the insights you can unlock from it. Statistical methods are strategies you can use to analyze and understand data. These methods help you predict the future such as forecasting sales or demand, optimizing business processes, exploring the feasibility of new ideas, and more. 

Explore types of statistical methods, including descriptive and inferential statistics, and the difference between parametric and non-parametric tests. 

What are statistical methods? 

Statistical methods are techniques in statistics that allow you to find patterns and subsequently understand meaningful insights about your data. You can use these tools to manipulate, organize, and analyze data. Such mathematical strategies perform sophisticated analysis of data in many different fields. However, people use statistical methods to analyze data in everyday life. 

For example, if you follow a budget for your personal finances, you might track how much you spend in each budget category during the month. When setting your budget for the following month, you can calculate the average amount you spent per category over the last couple of months. This number will give you a place to start based on real data, which you can then use to make decisions about your monthly spending. While the data you track is helpful, it doesn’t yet give you actionable insights until you apply the statistical method of finding the average. 

Statisticians and data scientists use statistical methods to test their hypotheses, understand the probability of outcomes, provide companies with insight into their customers, markets, and business processes, and create machine learning algorithms. The type of statistical method you use will impact the quality and reliability of your analysis. 

Why are statistical models important?

Selecting the appropriate statistical method for data analysis is important, as it influences your analysis and findings. This choice depends on the goal of your work, the kind of data you’re working with, and whether you have paired or unpaired data. Paired data measures a variable at two different points, providing insight into how the variable changed. Independent data points make up unpaired data. These factors will determine the type of statistical method you need, which generally falls into either descriptive or inferential statistics. 

Types of statistical models

The two main types of statistical models are descriptive and inferential. Descriptive statistics describe a set of data. To further expand on the personal finances example, calculating the average amount you spend in each budget category provides a clear and easy-to-understand summary of your data. Inferential statistics allow you to analyze a sample size and draw conclusions about the larger population or group. For instance, you could use inferential statistics to estimate how much you will spend in each budget category through the rest of the calendar year. 

Descriptive statistics

Descriptive statistics help summarize data. Descriptive statistical methods include measures of central tendency (mean, median, and mode), measures of variation (range, standard deviation, and variance), and other statistics that help describe the data (like nominal, ordinal, ratio, and interval). 

• Mean 

• Median

• Mode

• Range 

• Standard deviation

• Variance

• Nominal

• Ordinal

• Interval

• Ratio

Inferential statistics

Inferential statistics use a sample of data from a larger population and allow you to make inferences about the statistics of the entire population. Statisticians often use inferential statistical methods to test their hypotheses and are particularly helpful when it would be impossible or unwieldy to measure the entire data set. For example, if you wanted to know how people in your state voted in the last election, you might poll a random sample of people and analyze how the people within your sample voted. If you have a sample size that properly represents the greater population of your state, you could infer that your data is representative of the group of people you want to know about. Inferential statistics can be parametric or non-parametric. 

Parametric tests

Parametric statistical tests make assumptions about how a sample size will relate to the whole. For example, you might assume that the population follows a normal distribution, a bell curve where the majority of people fall into the category of average behavior, with some people falling below or above the average, tapering off at each end. Examples of parametric statistical methods include the Student's t-test, analysis of variance (ANOVA), repeated measures ANOVA, Pearson correlation coefficient, and linear regression.

• Student’s t-test

• ANOVA

• Repeated measure ANOVA

• Pearson correlation coefficient

• Linear regression

Non-parametric tests

When you’re unable to make assumptions about the population at large, you can use non-parametric methods to make inferences instead. Non-parametric tests are usually less powerful than parametric tests, but they can give you the flexibility to conduct inference analysis in a wider range of situations. You can also use non-parametric statistics to compare the proportions of two groups. Examples of non-parametric statistical methods include the sign test, Wilcoxon's signed rank test, Mann-Whitney test, Kolmogorov-Smirnov test, Kruskal-Wallis test, Jonckheere test, Friedman test, Chi-square test, Fischer's exact test, and McNemar's test.

• Sign test

• Wilcoxon’s signed rank test

• Mann-Whitney test

• Kolmogorov-Smirnov test

• Kruskal-Wallis test

• Jonckheere test

• Friedman test

• Chi-square test

• Fischer’s exact test

• McNemar’s test

Other types of inferential statistical analysis

You can also use statistical methods that fall between parametric and non-parametric methods. These are semi-parametric tests you can use to analyze situations where you can make assumptions about the variables in some regards but not in others. These methods include examples like the Cox proportional hazards model, logistic regression, and the receiver operating characteristic curve. 

• Cox proportional hazards model

• Logistic regression

• Receiver operating characteristic curve

Applications of statistical methods in research 

Statistical methods are critical to working with data and help data scientists provide insight to companies in many different industries and organizations, including business intelligence, law, health care, entertainment, government, and finance. Statistical methods are also important to your everyday life in a variety of ways, like predicting the weather or informing your favorite social media algorithm about what content you would like to see. 

Researchers use statistical methods in various ways, including: 

• You could analyze the results of a medical study about blood pressure treatments to test your hypothesis about how the treatment will affect patients. You would use statistical methods to describe how different groups react to the study and help to determine whether a difference between the results of each group exists. You could then use inferential statistics to make conclusions about how the entire population would respond to various treatment options. 

• You can use statistical methods to forecast future sales for your company’s product. You can additionally use qualitative methods, time series analysis projection, and causal models to create projections for demand based on historical data and other relevant information like seasonality and market strategy. 

Learn more about statistical methods with Coursera

Statistical methods are an important tool for statisticians and data scientists to understand and analyze data. If you want to learn more about working with statistical methods, you can find several resources on Coursera to help you begin. You can learn job-ready skills and prepare for a career with a Specialization, such as the Business Statistics and Analysis Specialization, where you’ll learn how to apply statistics and data analysis tools to various business applications.