What Are Some Common Statistical Measures In Data Analytics?

Many Statistical measures in data analytics are used in data analysis which can be used to understand data and make informative decisions. There are many different types of statistical measures used in data analytics. Without statistics analysis it is not possible to extract useful insights from the available data.

It is important to know how to use different statistical measures in data analysis and discover trends in your market. In this blog, we will learn what are some common statistical measures used in data analysis and its benefits and more.

Table of Contents

What is Statistical Analysis?

Statistical analysis is used to perform data collection, organisation, and interpret data. The major objective of statistical analysis is to extract insights, identify trends, and relationships in data. You can take important and strategic decisions based on the forecast and information available from data.

The complete statistical analysis can be used in various fields such as finance, healthcare, human resources, operations, product development, and more. With statistical analysis, businesses can extract data driven insights and build more effective strategies.

What is the Use of Statistical Analysis?

Statistical Analysis is an important part of data analysis where you can drive useful insights and work with data on various stages to make forecasts, read patterns, predict trends and more.

Informed Decision Making

With a powerful statistical analytics in data analytics method you can easily extract important insights and make informed decisions using the available data. This information can be collected and evaluated to make informed strategies and reduce risks of failure.

Understanding Relationships

It is important to understand the connection between available data and uncover the relationships between various variables available in the dataset. We can use regression analysis and factor analysis to uncover important relationships and patterns in data.

Predicting Future Trends

Statistical analysis in data analytics can be used for predictive analytics and forecast events based on data available. You can use different statistical measures and tools to forecast these events and help organisations tackle market trends and opportunities in the market. You will be ready to meet any demands and solve operational bottlenecks.

Improving Efficiency in Available Data

You can use different effective statistical measures to improve efficiency in extracting data and mitigate unnecessary data from the dataset. You can easily streamline the data analysis process using tools and methods.

Types of Statistical Measures In Data Analytics

Let us check different types of statistical analysis methods for data analysis given below.

Descriptive Statistical Analysis

This statistical analysis in data analytics is used to measure the central tendency using mean, median and mode. You can use dispersion range, variance, and standard deviation. It allows you to measure frequency distribution using bar charts, pie charts, and more.

Predictive Statistical Analysis

It can be used to measure regression using multiple regression and polynomial regression. You can also calculate time series analysis and machine learning techniques such as decision trees, random forests and neural networks.

Exploratory Data Analysis (EDA)

It is used to measure outlier detection using Z-scores and data visualisation using scatter plots and heatmaps. Also, you can perform data transformation using standardization and log transformation.

Inferential Statistical Analysis

This method is a statistical measure used for hypothesis testing, confidence intervals, regression analysis, correlation analysis and probability distribution, and more. You can use techniques like t-test, ANOVA, Chi-squares, Linear Regression, Normal, binomial distributions.

4 Major Statistical Analysis Processes Methods

Let us check some of the major statistical measures used to extract insightful data from the data.

1. Data Collection

In this method gathering relevant and accurate data from various sources (e.g., surveys, experiments, databases). It is important to ensure data quality by handling missing values, removing duplicates, and checking for inconsistencies.

2. Data Organization & Cleaning

This method focuses on structuring raw data into a usable format using spreadsheets, databases, or statistical software (like Python, R, or Excel). Also, handling outliers, missing values, and formatting inconsistencies.

3. Data Analysis & Interpretation

In this method we apply descriptive statistics (mean, median, mode) to summarize data and perform inferential statistics (hypothesis testing, regression analysis) to make predictions. We are also using data visualization techniques (charts, graphs, histograms) to interpret trends and patterns.

4. Data Reporting & Decision Making

This statistical measure is used to represent findings through reports, dashboards, and visual storytelling. Also, drawing actionable conclusions based on statistical insights and using results for informed decision-making in business, healthcare, finance, and other domains.

Methods In Statistical Measures Used in Data Analysis

Let us check some of the methods used in statistical measures in data analysis below.

1. T-Tests

A t-test determines whether there is a significant difference between the means of two groups. It is commonly applied when comparing the performance of two groups under different conditions.

There are different types of t-tests, including:

Independent t-test: Compares means of two unrelated groups.
Dependent t-test (paired t-test): Compares means within the same group before and after an intervention.

For example, in medical trials, a t-test helps analyze whether a drug improves patient conditions more effectively than a placebo.

2. Chi-Square Tests

Chi-square tests evaluate the relationship between categorical variables by comparing observed results with expected ones. The goal is to determine whether variations are due to chance or actual associations between variables.

For instance, a retail company might use a chi-square test to analyze whether product preferences vary by customer region. It is widely used in market research and survey analysis.

3. ANOVA (Analysis of Variance)

ANOVA compares the means of three or more groups to assess whether there are significant differences among them. It extends beyond t-tests, which only compare two groups.

Types of ANOVA include:

One-way ANOVA: Evaluates the effect of one independent variable on a dependent variable.
Two-way ANOVA: Examines the effects of two independent variables.
Multivariate ANOVA (MANOVA): Assesses more than two independent variables.

Businesses use ANOVA to compare customer satisfaction across multiple demographics or evaluate product performance in different regions.

4. Regression Analysis

Regression analysis determines the relationship between a dependent variable and one or more independent variables. It helps predict outcomes and identify key influencing factors.

Linear regression: Finds a straight-line relationship between variables.
Multiple regression: Includes more than one independent variable to predict an outcome.

For example, companies use regression analysis to forecast sales based on marketing expenses, product prices, and customer demographics.

5. Time Series Analysis

Time series analysis examines data points collected at regular intervals over time to identify trends and make forecasts.

Applications include:

Retail industry: Predicting inventory needs based on holiday sales trends.
Finance: Tracking stock market movements.
Energy sector: Monitoring power consumption patterns for efficient resource management.

This method helps businesses make data-driven predictions based on historical trends.

6. Survival Analysis

Survival analysis focuses on the time it takes for an event to occur, such as equipment failure, customer churn, or disease progression.

For example:

Healthcare: Predicting patient survival rates based on treatment plans.
Business: Estimating product lifespan and customer retention duration.

It is widely used in medicine, engineering, and customer analytics.

7. Factor Analysis

Factor analysis simplifies complex datasets by grouping related variables into fewer underlying components. It extracts shared variance from multiple variables to reveal hidden patterns.

Examples include:

Market research: Identifying key customer preference categories.
Product development: Determining the most valued product features from survey data.

This technique is useful for reducing data complexity while preserving meaningful relationships.

8. Cluster Analysis

Cluster analysis groups similar data points based on shared characteristics. It is widely used in customer segmentation and data classification.

Some of the applications include:

Marketing: Categorizing customers by demographics and purchasing behavior.
Education: Identifying student groups that require additional support.
Medicine: Grouping patients with similar symptoms for personalized treatments.

By identifying distinct groups, businesses can create targeted marketing campaigns and customized services.

9. Principal Component Analysis (PCA)

PCA is a dimensionality-reduction technique that transforms large datasets into fewer principal components, preserving essential information while eliminating redundancy.

Common applications include:

Finance: Identifying factors that drive market fluctuations.
Marketing: Determining the key drivers of customer purchasing behavior.
Genetics: Simplifying large biological datasets for analysis.

How to Choose the Right Statistical Analysis Method?

It is important to choose the right statistical analysis methods while choosing the one method below.

Identify the data and its types

There are many types of target groups in data and it is divided into many classified categories. Data can be expressed in numbers, weight, temperature and more.

For example, If you are analyzing survey responses on customer satisfaction (e.g., Yes/No answers), you would use chi-square tests for categorical data.

Numbers of Groups and Variables

You can compare two or more than two groups using t-test, ANOVA, and find relationships between variables using regression analysis. When you want to compare sales revenue across five different stores, an ANOVA test would be suitable.

Understanding the Relationship Between Variables

If you are exploring relationships then you can use regression analysis and correlation to measure associations and use cluster analysis for customer segmentation. Make use of time series analysis to forecast trends.

Define your Research Goals

Make sure you define your research goal which can be prediction, pattern recognition, comparison, grouping and more. You can use t-tests, chi-square tests, ANNOVA, regression, cluster analysis and more.

Count the Sample Size

Make sure you are familiar with the sample size of your data when the value is less than 30 then you can use t-tests and if it is more than 30 then use Z-tests or regression analysis methods.

Learn Data Analysis with PW Skills

Become an expert data analyst with an online certification Data Analysis course offered by PW Skills. Get in-depth tutorials under the mentorship of experienced professionals within the course. Master advanced data analytic tools and get your career to a new height with this 6 month online course.

Get interview opportunities, and elevate high fast-track career opportunities. Build your job portfolio with dynamic projects included only at pwskills.com

Q1. What are statistical measures?

Ans: Statistical analysis is used to perform data collection, organisation, and interpret data. The major objective of statistical analysis is to extract insights, identify trends, and relationships in data.

Q2. What are various statistical measures in data analysis?

Ans: Exploratory data analysis, descriptive data analysis, predictive analysis, inferential statistical data analysis and more are some of the methods used in statistical measures in data analysis.

Q3. What are four important statistical processes in data analysis?

Ans: The four important statistical processes in data analysis are data cleaning, data organisation, data analysis, data interpretation, and data reporting and presentation.

Q4. What is descriptive analysis?

Ans: This statistical analysis is used to measure the central tendency using mean, median and mode. You can use dispersion range, variance, and standard deviation.