# Histograms and Distributions

The log-scale histogram is one of the core [tile statistics](/docs/inspect-and-analyze/tensor-statistics) Tensormorph computes for every tensor, and it has its own dedicated 1D view for reading a value distribution directly rather than as a byproduct of a color scale.

## Why log-scale by default

Weight and activation values in a trained model are typically concentrated near zero with a long tail - a linear-scale histogram spends most of its bins on empty or near-empty space at the extremes and collapses the interesting detail near the center into a handful of bins. Tensormorph's default histogram bins on a log scale specifically to keep both the concentration near zero and the tail legible in the same plot; a linear view is available but isn't the default for exactly this reason.

## Reading a distribution

The Distribution view shows:

-   The binned histogram itself, as a bar plot.
-   Markers for the statistics computed alongside it - mean, standard deviation band, min/max/absmax - so you don't have to cross-reference the Inspector panel to see where a specific value sits relative to the shape of the distribution.
-   A cumulative curve, toggleable, for reading percentile-style questions ("what fraction of values fall below X") directly off the plot.

## Distribution at any granularity

Because the underlying histogram statistic is [mergeable](/docs/inspect-and-analyze/tensor-statistics#why-mergeable-matters) like every other tile statistic, the Distribution view works identically whether you've selected one tile, one tensor, or an entire layer - the histogram for a coarser selection is the merge of its children's histograms, not a fresh computation.

## Comparing two distributions

Selecting two tensors - most commonly the same tensor across two model revisions, or two experts in an MoE layer - overlays both histograms in the same plot rather than requiring two separate views. This is the histogram-level counterpart to a [weight diff](/docs/compare-and-morph/weight-diff): where a numeric diff shows *where* two tensors differ, an overlaid distribution comparison shows whether they differ in *character* (a shift in mean, a change in spread, a new mode appearing) even in regions where a per-element diff would be small.

## Related resources

-   [Tensor Statistics](/docs/inspect-and-analyze/tensor-statistics) - the full set of statistics computed alongside the histogram.
-   [Outliers and Anomalies](/docs/inspect-and-analyze/outliers-and-anomalies) - using a distribution's shape to flag values that don't belong to it.
-   [Signal and Distribution Editor](/docs/editors/signal-and-distribution-editor) - the 1D view family the Distribution view belongs to.