minor doc fixes

docs/user-guide/overview.md

@@ -2,7 +2,8 @@
 
 ## The Anscombe Transform
 
-The Anscombe Transform is a variance-stabilizing transformation specifically designed for data with Poisson noise. In photon-limited imaging, the noise variance equals the signal mean (characteristic of Poisson statistics), which makes compression difficult because different intensity levels have different noise characteristics.
+The Anscombe Transform is a variance-stabilizing transformation specifically designed for data with Poisson noise. In photon-limited imaging, the noise variance grows linearly with the signal mean (characteristic of Poisson statistics), 
+which makes compression difficult because different intensity levels have different noise characteristics.
 
 ### The Problem
 
@@ -93,9 +94,9 @@ The codec is designed for speed:
 ### Accuracy
 
 The codec is designed to be **nearly lossless** for photon-limited data:
-- Typical error: < 1 photon per pixel
-- Error scales with `conversion_gain` and quantization (`beta` parameter)
-- For well-chosen parameters, reconstruction error is below the noise floor
+- Max absolute error: ~ 0.25 noise-sigma per pixel (for `beta=0.5`)
+- Error scales with quantization (`beta` parameter)
+- For default parameters (`beta=0.5`), the noise variance is increased by a ~1 % with respect to the original noise variance and no bias is introduced.
 
 ## When to Use This Codec
 
@@ -109,10 +110,9 @@ The codec is designed to be **nearly lossless** for photon-limited data:
 
 ### Not Recommended ❌
 
-- Data without Poisson noise (e.g., pre-processed images)
-- Data where camera parameters are unknown and can't be estimated
-- Data with very high dynamic range (> 16-bit)
-- Data that has already been normalized or transformed
+- Data with non-Poisson or non-stationalry noise (e.g., pre-processed images)
+- Data where detector parameters are unknown and can't be estimated
+- Data that has been transformed with a non-linear function (e.g. gamma correction)
 
 ## Next Steps