Statistical Tests Explained: Best Tools, Online Software & AI Tips

Statistical tests are one of the most effective ways to find meaningful patterns, relationships, and differences in your survey data. What's more, in 2025, statistical testing is as accessible as ever, provided you have access to the right tools and methods.

But with so many different tools, methods, and software options available, choosing the right statistical testing option can feel overwhelming. Some methods are overly complicated, while certain tools can be too simple for specific data sets.

Why Do We Do Statistical Tests?

Many different scenarios warrant the use of statistical tests, and each of these situations may require specific methods. However, the most common reason we use statistical tests is to understand whether certain results are statistically significant.

Statistical significance means the result can not be explained solely by chance - i.e. it is not a fluke. Testing these results helps researchers discern genuine insights from statistical noise. This is quantified by what is called a p-value - a number that shows the probability of obtaining the observed results, if you were to assume the null hypothesis (there is no difference between two variables) is true.

But above all, statistical tests are a way to save time. For example, most survey analysis involves the creation of crosstabs - and lots of them. Statistical testing is how we automatically find the interesting results hidden within this plethora of crosstabs. This means less time spent interpreting customer feedback and any other surveys and more time finding insights.

Understanding Different Statistical Tests

The main statistical tests used to compute p-values are;

• t-tests - Determine if there is a statistically significant difference between the means of two groups.T-tests are used when the population standard deviation is unknown and estimated from the sample. They are ideal for showing if there is a real, statistically significant difference between two samples, or if it is due to chance. Common types include independent, paired, and one-sample t-tests. T-tests assume that the data is normally distributed.
• z-tests - Z-tests are used when the population standard deviation is known. With very large sample sizes, the t-distribution approaches the z-distribution. A z-test is a statistical test used to compare the means or proportions of large samples to determine if differences are significant. Both the z-test and the t-test assume a normal distribution of the data.
• Chi-square test - analyzes relationships between categorical variables in a dataset, determing if observed frequencies differ from expected frequencies by chance. It is used to check if the number of people who prefer one option as opposed to the other is statistically significant.
• Fisher's exact test - similar to chi-square, but works best on smaller sample sizes. Fisher's exact test determines whether a statistically significant association exists between two categorical variables. This test was created by famous statistician Sir Ronald Fisher, who wanted to test whether a woman could correctly distinguish between tea with milk poured first versus milk with tea poured first, leading to a foundational method in statistics.

A Simpler Way: Statistical Testing Tools and Software

The above list only scratches the surface of the breadth of statistical tests available to help researchers determine whether results are significant. Each test varies in complexity, and as a result, only a small proportion of commercial researchers understand the actual mechanics behind these tests. This is because there are tools that can do it for you!

Statistical testing tools automate the process, speeding up research and democratizing access to powerful statistical insights. Some of the best tools you can leverage to introduce statistical testing into your research workflow include:

Displayr

When it comes to automating statistical tests of significance, Displayr is a leader. Built-in smarts automatically choose between more than 50 different statistical tests based on the specific properties of your data. Displayr highlights significant results on your tables.

As well as highlighting significant results, Displayr automatically deletes uninteresting tables, saving you hours spent sifting through pointless crosstabs.

Although automation is an integral part of Displayr's statistical testing solution, it also allows users to customize the options however they see fit - levels of significance, multiple comparison correction, parametric versus nonparametric tests, and more.

Q Research Software

Similar to Displayr, Q Research Software automates significance testing, highlighting statistically significant results in tables. The platform automatically selects appropriate significance tests based on the selected data.

Q handles calculations of standard errors, confidence intervals, and t-values automatically. It also makes effective sample size adjustments for weights to all tests. In terms of customization, you can set your own significance level cut-offs.

Qualtrics

Qualtrics offers access to Stats iQ - a specialized statistical analysis tool available for purchase as an add-on tool. The product was previously known as Statwing. It can be used on survey projects, imported data projects, ad hoc employee research, and engagement studies.

Stats iQ performs ANOVA, T-test, and Chi-squared directly on collected survey data to determine if the results are statistically significant. The platform provides users the ability to choose the type of statistical test they would like to perform. Stats iQ has the same ability in a survey project as it does in a dashboard.

Minitab

Having recently turned 50, Minitab is one of the most long-standing statistical software tools available. First created as a statistical package to help students at Penn State University learn statistics, Minitab still exists today and is particularly popular among the manufacturing, healthcare, and education industries.

Minitab offers a range of statistical tests designed for quality control and process improvement. The software supports ANOVA, t-tests, regression analysis, and capability analysis. The platform is designed to handle statistical analyses relevant to quality and efficiency improvements.

Julius AI

While Minitab is an example of a statistical testing tool that has stood the test of time, there are also some relative newcomers to the industry. One example of this is Julius AI, an AI assistant designed specifically for statistical analysis.

Julius AI lets users upload their data (CSV, Excel, PDF, JSON, and images supported) into the platform. Once the data is in the workspace, you can prompt the tool to perform the task you need completed (e.g. identifying if there is a statistically significant difference between two variables). Julius AI then provides the relevant Python code to perform the task. It has been described as a combination of ChatGPT and Python.

DIY

While each of the options above comes with a cost (although free trials and freemium packages are usually avaiable), there is also the option to DIY your statistical testing, provided you have the relevant skills and knowledge.

Open-source programming languages like R and Python offer complete control and customization for statistical significance testing, allowing you to implement specific tests and methodologies, and provide a greater understanding of the underlying statistical principles. The R Stats Package, and Python libraries such as SciPy and StatsModels, enable advanced users to perform customized statistical significance testing.

Best Statistical Analysis Tools Online

When it comes to running statistical tests quickly and accurately, the right tools can make all the difference. Today, there is a wide range of statistical analysis tools online that allow researchers to perform advanced testing without needing to manually code in R or Python. These tools combine ease of use with the power of automated statistical testing, making them accessible to beginners while still offering the flexibility experts need.

The best statistical testing software shares a few key traits:

• Automation: The tool should automatically choose the right statistical test (t-test, chi-square, ANOVA, regression, etc.) based on your data.
• Accuracy: Look for platforms that handle issues like multiple comparison correction, weighting, and small sample sizes.
• Accessibility: Online tools should save time by removing the need for manual coding while still giving advanced users options to customize significance levels, effect sizes, and other parameters.

Modern tools like Displayr, Q Research Software, and Qualtrics Stats iQ fit this description, enabling researchers to run tests online and instantly highlight significant findings. For more specialized or technical workflows, software like Minitab or AI-based assistants such as Julius AI offer additional depth.

Whether you’re looking for a simple way to test survey results or need advanced statistical testing software for complex projects, these online solutions can help streamline your analysis and free up time to focus on insights rather than mechanics.

Best AI Prompts for Statistical Testing

As mentioned above, DIY statistical testing requires a certain understanding and proficiency in R or Python. That being said, advances in generative AI have made it much easier to write code for stat testing - in fact, it has changed how researchers and analysts approach statistical testing by allowing them to generate complex code snippets with simple text prompts.

AI-powered tools can now assist with writing R or Python scripts for t-tests, ANOVA, chi-square tests, and more—reducing the barrier to entry and making advanced statistical analysis more accessible to non-programmers.

As anyone who has used generative AI tools knows, the results you get are only as good as the prompts you put in. To help accelerate your research and identify statistical significance in your data with ease - or to better interpret some of your results - consider some of the following prompts:

• Which statistical test is best suited to compare the impact of two distinct marketing strategies on sales performance?"
• "Analyze and explain the results of a chi-square test evaluating the relationship between customer demographics and product choices."
• "Conduct a t-test to assess whether there is a significant difference between the averages of two independent groups in the dataset. Summarize the results."
• "Given two datasets for comparison, define a research hypothesis, select the most appropriate statistical test, and carry out hypothesis testing. Include p-values and confidence intervals in your analysis."
• "Determine the correlation strength between [column_x] and [column_y] in the dataset and interpret its significance."
• "Perform a t-test to evaluate whether there is a meaningful difference between the means of [column1] and [column2]. Explain the implications of the findings. Also, request the effect size to understand the magnitude of the difference."

Important Considerations for Statistical Testing

Statistical testing is about taking an educated guess. It is crucial to remember that statistical significance does not always equal practical significance. Effect size measures the magnitude of the effect, providing vital context to your findings. When conducting multiple statistical tests, the risk of false positives increases. Before performing any statistical test, it is essential to ensure data quality, properly clean your data, and verify that the assumptions of the chosen test are met.

FAQs

With built-in intelligence that selects the right statistical test for your data, Displayr takes the guesswork out of statistical testing. Try our interactive statistical testing tutorial and experience the smarter way to analyze survey data.