May 20th, 2026
7 Types of Statistical Analysis: Full Guide, Examples, and How-to
By Tyler Shibata · 15 min read
The 7 types of statistical analysis help you summarize data, make predictions, test hypotheses, find patterns, and understand cause-and-effect relationships. In this guide, I'll show you what each type does and when to use them for your data.
The 7 types of statistical analysis (with examples)
Statistical analysis methods fall into seven categories based on what you're trying to accomplish with your data. Some help you understand what already happened, others predict what might happen next, and a few tell you what actions to take.
Let’s take a look at the types below:
1. Descriptive statistical analysis
Descriptive statistical analysis summarizes and organizes data to show you what happened. You use it to calculate averages, ranges, frequencies, and distributions without making predictions or drawing conclusions beyond the dataset itself.
This type answers questions like "what were our average sales last quarter?" or "how many customers fall into each age group?" I use descriptive analysis whenever I need to understand what's in a dataset before doing anything more complex. It's the starting point for most projects because you can't interpret trends or test hypotheses until you know what your data looks like.
Common descriptive methods include mean, median, mode, standard deviation, and frequency tables. If you've ever opened a spreadsheet and calculated an average, you've done descriptive analysis.
2. Inferential statistical analysis
Inferential statistical analysis uses sample data to make conclusions about a larger population. You collect data from a subset of people or cases, then use probability theory to estimate whether your findings apply more broadly.
This type answers questions like "if 60% of our surveyed customers prefer feature A, what's the likely preference across all customers?" I rely on inferential analysis when I can't measure an entire population but need to make confident claims about it. The math tells you how reliable your conclusions are and whether your sample size was large enough to trust the results.
Common inferential methods include t-tests, ANOVA, chi-square tests, and confidence intervals. These techniques calculate the probability that your sample findings reflect true population patterns rather than random chance.
3. Predictive statistical analysis
Predictive statistical analysis uses historical data to forecast future outcomes. You identify patterns in past behavior, then apply those patterns to estimate what might happen next under similar conditions.
This type answers questions like "how many support tickets should we expect next month?" or "which customers might churn in the next quarter?"
You can use predictive analysis when planning resources or prioritizing outreach, but be careful not to treat predictions as guarantees. Models work best when future conditions resemble past ones, and they may break down when circumstances change unexpectedly.
Common predictive methods include regression analysis, time series forecasting, and machine learning algorithms. The output usually includes both a prediction and a confidence range showing how certain the model is.
4. Prescriptive statistical analysis
Prescriptive statistical analysis recommends specific actions based on data, constraints, and goals. You feed the system your objectives and limitations, and it calculates the optimal decision or strategy to achieve the best outcome.
This type answers questions like "how should we allocate our ad budget across channels to maximize conversions?" or "which warehouse locations minimize shipping costs?" I use prescriptive analysis when there are too many variables to evaluate manually and when decisions have measurable trade-offs. It goes beyond showing what might happen and tells you what to do about it.
Common prescriptive methods include optimization algorithms, simulation models, and linear programming. These techniques calculate the best possible outcome given your goals and constraints.
5. Exploratory statistical analysis
Exploratory statistical analysis examines data to find unexpected patterns, relationships, or anomalies without testing specific hypotheses. You look for interesting signals in the data before deciding what questions to ask or what analyses to run next.
This type answers questions like "are there any unusual clusters in our customer data?" or "what variables seem related to each other?" I use exploratory analysis at the start of projects when I don't yet know what matters or where to focus. It's about discovery rather than confirmation, and it often reveals questions I didn't think to ask.
Common exploratory methods include scatter plots, correlation matrices, clustering, and outlier detection. The goal isn't to prove anything but to spot leads worth investigating further.
6. Causal statistical analysis
Causal statistical analysis determines whether one variable directly causes changes in another. You design studies or use advanced techniques to isolate cause-and-effect relationships and rule out coincidence or confounding factors.
This category of statistical analysis answers questions like: "Does our new checkout flow actually increase purchases, or did something else change at the same time?" I use causal analysis when I need to know whether an action I take will produce specific results. Correlation alone can't tell you this because two things might move together without one causing the other.
Common causal methods include randomized controlled trials, A/B tests, regression discontinuity, and instrumental variables. These approaches control for outside factors to measure the true impact of one variable on another.
7. Associational statistical analysis
Associational statistical analysis measures relationships between variables without claiming one causes the other. You calculate how strongly two or more variables move together, which helps you spot patterns even when you can't prove causation.
This type answers questions like "do higher engagement rates tend to coincide with longer session times?" or "are temperature and ice cream sales related?" Associational analysis shows you which variables move together, which can guide deeper research or inform predictions. But don't confuse correlation with causation, since two things can relate without one causing the other.
Common associational methods include correlation coefficients, chi-square tests for independence, and scatter plot analysis. The results show you whether a relationship exists and how strong it is, but they don't explain why.
Which type of statistical analysis should you use?
The type of statistical analysis you should use depends on what you're trying to accomplish with your data. Here's when to use each approach:
Use descriptive analysis when: You need to summarize what happened in your dataset. Calculate averages, totals, frequencies, or distributions to understand your data before moving to more complex methods.
Use inferential analysis when: You have sample data but need to make claims about a larger population. This works when you can't measure everyone but need reliable estimates about the whole group.
Use predictive analysis when: You want to forecast future outcomes based on historical patterns. I use this for planning resources or estimating demand, but predictions only work well when future conditions resemble the past.
Use prescriptive analysis when: You need to know what action to take. Feed the system your goals and constraints, and it recommends the optimal decision to achieve the best outcome.
Use exploratory analysis when: You don't have a specific hypothesis yet. This helps you discover unexpected patterns, clusters, or anomalies worth investigating further.
Use causal analysis when: You need to prove that one variable directly causes changes in another. This requires experiments or advanced techniques to rule out coincidence and confounding factors.
Use associational analysis when: You want to measure how strongly variables relate without proving causation. This helps you spot patterns for further investigation or build predictive models.
How to conduct statistical analysis in Julius
Data analysis tools can run these methods for you once your data is ready. For this walkthrough, I'll use Julius, which lets you run analysis by typing your question in plain English.
Here's how:
Step 1: Get your data into Julius
Start by getting your data into Julius. You can start from a question and let Julius pull public datasets or financial data directly, upload a CSV or Excel file by dragging it into the chat, or connect a data source like Google Sheets, Postgres, Snowflake, or BigQuery if your data lives there.
Step 2: Ask your question in plain English
Type what you want to know. Something like "what's the average revenue by region?" or "run a regression to predict next quarter's signups" or "show me correlations between engagement metrics" is enough to get started. Julius writes and runs the code in the background, so you don't need to know how to code to get results.
Step 3: Review your results
Julius returns your analysis with charts, tables, and a plain-English explanation of what the numbers mean. You can see the underlying code if you want to verify the method, or you can hide it if you're focused on the insights.
Step 4: Refine or dig deeper
If the first result isn't quite what you need, ask follow-up questions in the same chat. Julius remembers the context, so you can iterate without re-uploading data or starting over. It learns your data structure as you work, which makes follow-up queries faster and more accurate.
Step 5: Export or share your results
Once you have your analysis, you can download it as a CSV, image, or PDF, or share it directly from Julius via a link. You can also schedule recurring analyses to run automatically and send results to email or Slack.
Want to run statistical analysis without writing code? Try Julius
Running the different types of statistical analysis doesn't have to mean writing code or hiring a data scientist. With Julius, you can go from raw data to results by typing your question in plain English.
Here’s how Julius helps:
Data search: Julius can search the web for public datasets or pull structured financial data for 17,000+ companies via its Financial Datasets integration, so you can start from a question rather than an upload.
Direct connections: Link databases like PostgreSQL, Snowflake, and BigQuery, or integrate with Google Ads and other business tools. You can also upload CSV or Excel files. Your analysis can reflect live data, so you’re less likely to rely on outdated spreadsheets.
Repeatable Notebooks: Save an analysis as a notebook and run it again with fresh data whenever you need. You can also schedule notebooks to send updated results to email or Slack.
Smarter over time: Julius includes a Learning Sub Agent, an AI that adapts to your database structure over time. It learns table relationships and column meanings as you work with your data, which can help improve result accuracy.
Quick single-metric checks: Ask for an average, spread, or distribution, and Julius shows you the numbers with an easy-to-read chart.
Built-in visualization: Get histograms, box plots, and bar charts on the spot instead of jumping into another tool to build them.
One-click sharing: Turn an analysis into a PDF report you can share without extra formatting.
Ready to start your statistical analysis? Try Julius for free today.
Frequently asked questions
What is statistical analysis?
Statistical analysis is the process of collecting, organizing, and examining data to find patterns, test hypotheses, and draw conclusions. You apply mathematical and statistical methods to raw data to turn it into something meaningful and actionable. It's used across fields like business, science, healthcare, and finance to support better decision-making.
What are statistical analysis techniques?
Statistical analysis techniques are the specific methods you use to examine data, such as t-tests, regression analysis, ANOVA, correlation coefficients, and chi-square tests. Each technique serves a different purpose depending on your data type and what you're trying to find out.
What is the most commonly used type of statistical analysis?
Descriptive statistical analysis is the most commonly used type because it forms the foundation of almost every data project. Before you can test hypotheses, build models, or make predictions, you need to understand what your data looks like. Calculating averages, frequencies, and distributions gives you that baseline understanding before moving to more complex methods.
