simple-statistics Connector for AI agents.

It uses standard statistical methods like Z-Score and IQR to calculate how far each data point sits from the average. If a point is far enough away, the engine flags it for you.

Yes. Because the math happens locally on your machine, you can scan thousands of rows without worrying about your AI client's memory limits or context window.

Z-Score is best for data that follows a normal bell curve. IQR is a more robust method that works better for skewed data, like income or real estate prices.

Absolutely. It helps you identify and remove 'poison' data points that could skew your models and lead to inaccurate predictions.

You can tell your agent exactly what threshold to use, such as a Z-Score higher than 3 or a specific multiplier for the IQR method.

Yes, by using the IQR method. This is the preferred way to find anomalies in data that isn't perfectly balanced or normally distributed.

Z-Score assumes data is normally distributed and is sensitive to extreme outliers. IQR is based on percentiles (25th and 75th), making it robust and ideal for skewed or non-normal data.

Yes! You set the threshold parameter: typically 3 for Z-Score (flagging values beyond 3 standard deviations) or 1.5 for IQR (the standard Tukey fence multiplier).

No. The engine flags the outliers and provides their exact Z-Scores or IQR bounds so the AI can report them to you. The decision to drop or keep them remains with you.