If you want to simulate measurement noise or test calculations with variability, Add Errors to Numbers lets you introduce random error into any list of numbers. It supports both absolute and percentage error types and updates instantly as you tweak the settings.
Paste in numbers line-by-line or comma-separated, choose how much variation to apply, and decide whether to round the result or show the original values alongside the noisy ones. It’s great for quick simulations, error modeling, or dataset generation.
How to Use:
- Paste a list of numbers into the Input Numbers box.
- You can also import from a
.txt
,.csv
, or similar file using the Choose File button. - Adjust the Options:
- Select Error Type:
- Absolute (e.g., ±5)
- Percentage (e.g., ±10%)
- Set the Max Error value (e.g.,
2
for ±2 or5
for ±5%) - Toggle Round to 2 decimals to control output precision
- Toggle Include original values to show side-by-side comparison (
original → noisy
)
- Select Error Type:
- The output updates live on the right.
- Use the buttons to copy, export, or clear your data.
What Add Errors to Numbers can do:
The tool applies a random error to each value in your list. It chooses a new number within the range you specify either as a fixed amount or a percent of the original – and applies it positively or negatively at random. You can control formatting and how much context is included.
Example:
Input:
100
200
3.14
Settings: Type: Percentage, Max Error: 10, Round: ON, Show original: ON
Output:
100 → 106.22
200 → 187.98
3.14 → 3.34
Common Use Cases:
This is useful for generating synthetic test data, simulating sensor noise, modeling uncertainty, or preparing randomized datasets for training or validation. It’s especially handy for teachers, data scientists, and engineers who want fast, flexible number variation.
Useful Tools & Suggestions:
If you’re scrambling numbers on purpose, Perturb Number Digits gives you more structured digit-level changes. And Add Errors to Numbers is great when you want to tweak values numerically but still keep them believable.