FAQ - Frequently Asked Questions

StatLabo is an online statistical analysis platform (SaaS) that allows businesses and individuals to easily import, analyze and visualize their data. Our tool offers advanced features for descriptive statistics, industry benchmarking and marketing optimization, all without requiring programming skills.

Yes! StatLabo offers a free plan with basic data analysis features. For more advanced needs, we offer paid plans with extended features, priority support, and greater processing capabilities. You can also support us through donations to help develop new features.

To access all analysis features, you need to create a free account. Registration is quick and only requires an email address. This allows you to save your analyses, access your data history and customize your dashboard.

StatLabo accepts CSV, Excel (.xlsx, .xls) and JSON files. We support files up to 50 MB for the free plan and up to 500 MB for premium plans. Data is automatically validated and prepared for analysis upon import.

Your data security is our priority. All communications are encrypted via HTTPS/TLS. Your data is stored on secure servers in Europe and is never shared with third parties. You can request complete deletion of your data at any time.

Yes, StatLabo allows you to export your analyses in multiple formats: PDF for reports, Excel for data tables, and PNG/SVG images for charts. Exports are available with one click from each analysis module.

Yes, our interface is fully responsive and optimized for tablets and smartphones. You can view your dashboards and analysis results from any device. However, for a better data import experience, we recommend using a computer.

The mean is the sum of values divided by their count (sensitive to outliers). The median is the central value that divides a set into two equal parts (resistant to outliers). For asymmetric data (income, prices), the median is often more representative.

Standard deviation (σ) measures how spread out data is around the mean. A low standard deviation indicates homogeneous, clustered data; a high standard deviation indicates heterogeneous, spread out data. It is the square root of variance.

The p-value is the probability of obtaining a result at least as extreme as the observed one, if the null hypothesis were true. A p-value < 0.05 is generally considered statistically significant, allowing rejection of the null hypothesis.

No! A correlation between two variables does not prove that one causes the other. There may be confounding variables that influence both. For example, the correlation between ice cream sales and drownings is explained by summer heat, not a direct causal link.

A confidence interval (CI) is a range of values that likely contains the true value of the estimated parameter. A 95% CI means that if sampling were repeated 100 times, about 95 intervals would contain the true value.

Linear regression models the relationship between a dependent variable (Y) and one or more independent variables (X) as an equation: Y = aX + b. It allows predicting values and quantifying the impact of each explanatory variable.

R² measures the proportion of Y's variance explained by the model. An R² of 0.80 means the model explains 80% of data variability. The closer R² is to 1, the better the model fits.

The Chi-square test (χ²) analyzes relationships between categorical variables. It compares observed frequencies to expected frequencies under the independence hypothesis. A significant result indicates an association between variables.

ANOVA (Analysis of Variance) compares the means of three or more groups to determine if significant differences exist. It partitions total variance into between-group and within-group variance.

An outlier is an observation that significantly deviates from other data. Outliers can skew descriptive statistics (mean, standard deviation) and should be identified and addressed (correction, removal, or separate analysis).

The normal distribution (or Gaussian) is a bell-shaped probability distribution, symmetric around the mean. It is fundamental in statistics because many natural phenomena and the sum of random variables tend toward this distribution.

Sample size depends on desired precision, data variability, and required confidence level. Generally, larger samples yield more precise estimates. A rule of thumb suggests at least 30 observations to invoke the central limit theorem.

Log into your account, go to 'Data Import', then drag and drop your file or click to select it. Accepted formats are CSV, Excel and JSON. StatLabo will automatically detect columns and their types.

Check that your file is in a supported format (CSV, Excel, JSON) and doesn't exceed the size limit. Make sure the file isn't corrupted by opening it locally. If the problem persists, contact our support with the displayed error message.

Send an email to contact@statlabo.com with a detailed description of the problem, steps to reproduce it, and if possible a screenshot. Include the browser used and any error messages displayed.

StatLabo works on all modern browsers: Chrome, Firefox, Safari, Edge. We recommend using the latest version of your browser for an optimal experience. Internet Explorer is not supported.

On the login page, click 'Forgot password'. Enter your email address and you'll receive a link to create a new password. The link expires after 24 hours for security reasons.

Yes, you have the right to delete your account and all associated data. Go to Settings > My Account > Delete Account, or send a request to contact@statlabo.com. Deletion is irreversible and takes effect within 48 hours.

You can reach us by email at contact@statlabo.com. We typically respond within 24-48 business hours. For premium users, priority support is available with reduced response time.

Yes, all your analyses are automatically saved to your account. You can find them in your history and reopen them anytime. Data is retained for 1 year of inactivity for the free plan.

StatLabo allows you to analyze your sales, marketing, HR or operational data to identify trends, optimize processes and make data-driven decisions. Our modules are designed for concrete business use cases.

You can analyze campaign performance (open rates, conversions), segment your audience, predict customer behavior, measure marketing ROI and identify the best-performing channels.

Import your sales data and use our modules to: analyze time evolution, identify most profitable products, segment customers by purchasing behavior, and predict future sales with regression.

Yes, our industry benchmarking module allows you to compare your key performance indicators (KPIs) to your industry averages. Import your data and select your sector to get a detailed comparative analysis.

Currently, integration is done via file import/export. We're working on direct connectors with Google Analytics, Shopify, and other popular tools. Contact us for specific integration needs.

Yes, we offer training on data analysis and business-applied statistics. Our workshops combine theory and practice with our tools. Check our Training section or contact us for custom training.

Each module provides explanations and interpretations of results. To go further, check our blog guides, Training section, or use our AI assistant that can answer questions about your analyses.

Your business data is strictly confidential. It is never shared, sold or used for purposes other than your analysis. We are GDPR compliant and you retain full control over your data.