# Outlier Detector Detect outliers in numerical data using various statistical methods including IQR, Z-score, and modified Z-score > Canonical page: https://elysiatools.com/en/tools/outlier-detector - **Category:** Data Analysis - **Keywords:** outlier, anomaly, detection, statistics, data, analysis, z-score, iqr ## Overview The Outlier Detector is a professional data analysis tool designed to identify anomalies in numerical datasets using robust statistical methods such as IQR, Z-score, and modified Z-score. ## Inputs - **Numerical Data** (textarea): Enter numerical data separated by delimiter... 12.5, 15.2, 13.8, 45.6, 18.9, 16.1, 14.7, 17.3, 22.1 - **Data Delimiter** (select) - **Custom Delimiter** (text): Enter custom delimiter - **Detection Method** (select) - **Sensitivity Threshold** (number): Detection sensitivity (1-3) - **Include Statistical Details** (checkbox) - **Output Format** (select) ## When to use - Cleaning raw datasets to remove noise before performing statistical modeling. - Identifying unexpected spikes or drops in financial or sensor performance data. - Validating data integrity by spotting values that deviate significantly from the norm. ## How it works - Paste your numerical data into the input field and select the appropriate delimiter used in your dataset. - Choose a detection method, such as Interquartile Range (IQR) or Z-score, based on your data distribution. - Adjust the sensitivity threshold to define how strictly the tool should flag potential anomalies. - Select your preferred output format to receive a clear summary, table, or JSON report of the detected outliers. ## Use cases - Financial auditing to detect irregular transaction amounts. - Quality control in manufacturing to identify faulty sensor readings. - Academic research to filter out measurement errors from experimental results. ## Frequently asked questions ### What is the difference between IQR and Z-score? IQR is robust against extreme values and works well for skewed data, while Z-score measures how many standard deviations a point is from the mean, making it ideal for normally distributed data. ### Can I use custom delimiters for my data? Yes, select the 'Custom' option in the delimiter dropdown and enter your specific character in the custom delimiter field. ### What does the sensitivity threshold do? The sensitivity threshold determines the strictness of the detection; a lower value flags more data points as outliers, while a higher value is more conservative. ### Which output format should I choose? Use 'Summary' for a quick overview, 'Table' for structured viewing, or 'JSON' if you need to integrate the results into other software. ### Is my data stored on your servers? No, this tool processes your data locally in your browser to ensure your information remains private and secure. ## Related tools - [Data Distribution Analyzer](https://elysiatools.com/en/tools/distribution-analyzer): Comprehensive data distribution analysis with normality tests, outlier detection, and goodness-of-fit assessments - [Skewness Analyzer](https://elysiatools.com/en/tools/skewness-analyzer): Analyze data skewness to measure the asymmetry of probability distribution and identify data patterns - [Standard Deviation Analyzer](https://elysiatools.com/en/tools/standard-deviation-analyzer): Comprehensive standard deviation analysis with variability assessment, confidence intervals, and practical insights - [Data Outlier Processor](https://elysiatools.com/en/tools/data-outlier-processor): Advanced outlier detection and processing tool that identifies, removes, or replaces anomalous values in numerical data using multiple statistical methods. Perfect for data cleaning, statistical analysis, and machine learning data preparation. Features: - Multiple detection methods (IQR, Z-score, Modified Z-score, Isolation Forest) - Flexible handling strategies (Remove, Replace with mean/median/mode, Cap) - Automatic threshold optimization - Multi-dimensional outlier detection - Visual outlier statistics and reporting - Batch processing capabilities - Custom sensitivity levels - Comprehensive impact analysis Common Use Cases: - Data cleaning and preprocessing - Statistical analysis preparation - Machine learning dataset cleaning - Quality control in manufacturing - Financial anomaly detection - Sensor data validation - [Newton-Raphson Root Finder](https://elysiatools.com/en/tools/newton-raphson-root-finder): Newton-Raphson iteration for f(x) = 0 with analytic or numeric derivative, full iteration table, and explicit convergence/divergence reporting. - [Array Element Counter](https://elysiatools.com/en/tools/array-element-counter): Count occurrences of specific elements in arrays with support for multiple counting modes and statistical analysis - [Audio Dropout Detector](https://elysiatools.com/en/tools/audio-dropout-detector): Detect brief signal dropouts and silent gaps in audio - [Time Series Anomaly Detector](https://elysiatools.com/en/tools/time-series-anomaly-detector): Upload CSV or JSON time series data, detect anomalies with Z-Score and IQR methods, and return a chart-backed report ## Samples - [Regex Replace Samples](https://elysiatools.com/en/samples/regex-replace): Collection of common and useful regex replacement patterns for text transformation and data cleaning - [PDF Samples](https://elysiatools.com/en/samples/pdf-samples): Generated PDF samples from tools dated 2026-02-01 to 2026-02-10 - [Test Pyramid Examples - Testing Strategy Guide](https://elysiatools.com/en/samples/test-pyramid-examples): Comprehensive test pyramid implementation examples including unit tests, integration tests, E2E tests, test organization, and strategic testing patterns for balanced software quality assurance - [Markdown Samples](https://elysiatools.com/en/samples/markdown-samples): Markdown format examples from simple to complex document structures ## Related content - [Data Quality and Anomaly Investigation Workflows](https://elysiatools.com/en/hubs/data-quality-anomaly-workflows): Profile tabular data, isolate duplicates, missingness, referential breaks, outliers, and time-series anomalies, and turn findings into defensible quality evidence.