Same formula, different axis; batch statistics wobble when batches shrink.
Batch norm standardises each feature across the examples in a batch; layer norm standardises each example across its own features. The outlined group shares one mean and one deviation. Shrink the batch: batch-norm statistics jump about and at two examples every column becomes ±1, while layer-norm statistics stay put. Amber is positive, blue negative; values are illustrative.