Start Your Analysis with 250 Free Wistats Credits
Receive 250 credits instantly upon registration. Complete a full analysis of a small project in seconds.
Credits are granted once per new account. Terms and conditions apply.
Upload your data to Wistats and quickly create your Statistical Output Texts for publication!
Hundreds of research texts have been generated with Wistats!
Researchers worldwide use Wistats to produce publication-ready statistical result texts in just a few minutes.
Upload your data and set your parameters!
No coding or manual setup required — Wistats processes your data and automatically determines the most suitable tests.
The system automatically determines which statistical test to use!
Wistats intelligently selects the most appropriate statistical methods based on your variable types and study design.
Automatic combination analyses for categorical variables with more than two options!
For example, in a categorical variable with five options, Wistats automatically generates 2-, 3-, and 4-way combination analyses and applies comparative statistical tests.
Automatically prepares publication-ready statistical result texts!
Outputs are generated in scientific writing format — ready to be inserted directly into your manuscript.
Texts are generated in both English and Turkish!
Switch between languages with a single click and obtain bilingual outputs for international journals.
Save valuable time!
Access all statistical results required for your paper within minutes instead of hours.
No need for advanced statistical knowledge!
Wistats automates the entire analytical process — you focus on the science, we handle the statistics.
Wistats: A Complementary Part of the Scientific Analysis Ecosystem
Wistats is not a competitor to other analytical platforms; it is a complementary solution that supports different stages of the research process by integrating statistical analysis and automated text generation.
Discover how Wistats turns your data into publish-ready statistical outputs in seconds.
What is Wistats Credit?
Pricing
Credits are calculated based on total dataset size and the complexity of analysis actions.
| Dataset Cell Count | Credits Required | Credit Definition |
|---|---|---|
| 0 – 99 cells | 10 credits |
1 credit = 1 descriptive statistic trigger or 1 comparative analysis Dynamic ML analysis credits are deducted based on: (Number of ML models × Input-variable combination count) |
| 100 – 499 cells | 25 credits | |
| 500 – 999 cells | 50 credits | |
| 1,000 – 9,999 cells | 100 credits | |
| 10,000 – 24,999 cells | 150 credits | |
| 25,000 – 99,999 cells | 200 credits | |
| 100,000 – 249,999 cells | 250 credits | |
| 250,000 – 499,999 cells | 300 credits | |
| 500,000 – 749,999 cells | 350 credits | |
| 750,000 – 1,000,000 cells | 400 credits |
💡 Machine learning analyses dynamically consume credits depending on model count and input-feature combinations.
Credit Calculation Example
When a dataset with 200 rows and 10 columns is uploaded:
| Dataset Upload | Descriptive Statistics | Comparative Analysis | Total |
|---|---|---|---|
| 100 credits | 10 credits | 45 credits | 155 credits |
💡 Explanation: In this example, a dataset with 200×10 cells was processed with 100 credits. Then, 10 descriptive statistics and 45 comparative analyses were performed, resulting in a total of 155 credits used.
Models and Analyses Used in Wistats
Wistats automatically applies advanced statistical and machine learning models to produce reliable results in scientific analysis processes.
Descriptive Statistics
Automatically applied to numerical and categorical variables.
- Mean (standard deviation)
- Median
- Minimum
- Maximum
- Sum
- Frequency
Skewness and Kurtosis
Evaluates deviation or asymmetry from the mean in numerical data distributions.
- Skewness
- Kurtosis
Outlier Analyses
Analyzes the presence of outliers in numerical variables.
- z-score
- Interquartile range (IQR)
Normality Tests
Evaluates the normality of numerical variables.
- Kolmogorov–Smirnov
- Shapiro–Wilk
Comparative Analyses
Tests are automatically selected based on variable type (categorical–numerical) and data distribution.
- Mean-based comparison
- Frequency-based comparison
- Correlation-based comparison
- Automatic combination analyses for categorical variables with more than two options
Between-Group Difference Tests
Appropriate tests are automatically selected and applied based on data distribution and variable properties.
- Chi-square
- Fisher Exact
- t-test
- Oneway ANOVA
- Kruskal–Wallis
- Mann–Whitney U
Correlation Analyses
Applied to examine the relationships between pairs of numerical variables.
- Pearson
- Spearman
Classification-Based Machine Learning Models
Dynamically applied in categorical classification analyses.
- Decision Tree
- Random Forest
- Support Vector Machine
- k-Nearest Neighbors
- Logistic Regression
Regression-Based Machine Learning Models
Dynamically applied in regression analyses for numerical data prediction.
- Decision Tree
- Random Forest
- Support Vector Machine
- k-Nearest Neighbors
- Linear Regression
Scientific Analysis Ecosystem: Wistats and Complementary Tools
Wistats, ChatGPT, SPSS, R, Python, MATLAB, and Stata are not competitors but complementary components of the same scientific ecosystem. Each tool contributes a unique capability to different stages of the research process.
Wistats
Central AI-Based Analysis and Text Generation Platform
- Analyzes data and generates scientific text using its own AI algorithms.
- Operates independently without relying on SPSS or R outputs.
- Transforms analytical results into publication-ready text, tables, and figures.
Role: The core component that unifies analysis, modeling, and writing in a single platform.
ChatGPT
Natural Language Assistant
- Supports explanation, interpretation, and methodological reasoning in statistical analyses.
- Generates and enhances sections such as introduction, discussion, and conclusions.
Role: The linguistic and interpretive layer that enriches analytical narratives.
SPSS
Classical Statistical Software
- Widely used in clinical and social sciences as a menu-based analysis environment.
- Performs descriptive statistics, regression, and hypothesis testing with standardized workflows.
Role: A visual, GUI-based classical analysis engine.
R
Open-Source Statistical Language
- Provides advanced modeling, visualization, and statistical computation capabilities.
- Serves as a widely adopted standard for data science and academic research.
Role: A flexible, programmable environment for scientific modeling.
Python
Core Data Science Language
- Provides analytical infrastructure through libraries such as NumPy, SciPy, and scikit-learn.
- Forms the computational backbone of the Wistats analytical engine.
Role: The foundation for scientific computation and automation.
MATLAB
Engineering and Modeling Platform
- Delivers powerful numerical computation, signal processing, and modeling capabilities.
- Widely used in academic, biomedical, and engineering research.
Role: A specialized environment for computational and engineering sciences.
Stata
Econometric Analysis Tool
- Commonly used in economics, health, and social sciences for regression and panel data analysis.
- Combines statistical modeling with a user-friendly, programmable interface.
Role: A robust environment for econometric and statistical modeling.
Conclusion: Wistats represents the central hub of the data science ecosystem. Rather than competing with ChatGPT, SPSS, R, Python, MATLAB, or Stata, it complements their strengths by providing an integrated platform that automates both analysis and scientific writing.
Comparison of Analytical and AI Tools in the Scientific Ecosystem
Each platform contributes uniquely to research and analysis. Wistats integrates the analytical power of Python-based computation with AI-driven text generation, bridging the gap between analysis and publication.
| Feature / Capability | Wistats | ChatGPT | SPSS | R | Python | MATLAB | Stata |
|---|---|---|---|---|---|---|---|
| Core Function | AI-based data analysis and scientific text generation | Language understanding and text generation | Menu-based statistical analysis | Programmable statistical modeling | Scientific computation and automation | Engineering and numerical modeling | Econometric and regression modeling |
| Requires Programming | No (automated interface) | No (text-based) | No (GUI-based) | Yes | Yes | Yes | Partial |
| Statistical Test Automation | ✔ Fully automated | ✖ No computation | ⚪ Manual selection | ⚪ Script-based | ⚪ Library-based | ⚪ User-defined | ⚪ Command-based |
| Machine Learning Integration | ✔ Built-in models (SVM, RF, LR) | ⚪ Descriptive support only | ⚪ Limited | ✔ Extensive | ✔ Extensive | ✔ Specialized | ⚪ Limited |
| Text Generation | ✔ Scientific text based on analysis results | ✔ General-purpose text generation | ✖ None | ✖ None | ⚪ Requires integration | ✖ None | ✖ None |
| Graphical Interface | ✔ Modern web dashboard | ⚪ Conversational UI | ✔ GUI | ✖ Script-based | ✖ Code-based | ✔ GUI | ✔ GUI |
| Result Reproducibility | ✔ Fully reproducible via internal engine | ⚪ Varies by prompt | ✔ Stable | ✔ Script reproducibility | ✔ Script reproducibility | ✔ Stable | ✔ Stable |
| Best Suited For | End-to-end scientific reporting | Writing assistance and explanation | Applied research and surveys | Academic and open-source research | Development and automation | Engineering, modeling, simulations | Econometrics and health statistics |
Summary: Wistats complements all other platforms by unifying data analysis and scientific writing, ensuring reproducibility and automation without requiring programming knowledge.
Looking for a Consultant?
If you are seeking expert consultancy for report preparation, manuscript development, statistical analysis, machine learning, or web-based decision support systems, please register and contact us through our support page.
Frequently Asked Questions
Sometimes numerical data may actually represent groups. In this case, which column type should I choose?
Should I specify categorical cells in the data set numerically or with group names?
Will there be a credit loss when I reload my dataset?
How are comparative statistics article text outputs produced?
Does it use ChatGPT or a similar application when producing statistical output texts?
How should I indicate in the method section of the article text that I used this system?
How can I use the Wistats app with a consultant?
Can you find me a consultant for my data analysis process?
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