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 **3)** Produce the category normalization weights for the training sample with **Normalization_Weights.C** and save the output to a text such as //QCD_normweights.txt//. These will be added as a weight branch "weight_norm" which flattens the vertex category distribution for the training sample.
-**4)** Assuming the evaluation sample vertex category weights have been produced (look at procedures for Evaluation Samples), add the normalization and category weight branches to the flat ntuples with **addWeightBranch.py**. The combination of these weights will remove the training sample vertex category information and match it with that of the evaluation sample.
+**4)** Assuming the evaluation sample vertex category weights have been produced (look at procedures for Evaluation Samples, step 3), add the normalization and category weight branches to the flat ntuples with **addWeightBranch.py**. The combination of these weights will remove the training sample vertex category information and match it with that of the evaluation sample.
 **5)** Create 2D Pt/Eta Histograms for the weighted ntuples with **createEtaPtWeightHists.py** (make sure "weight_norm*weight_category" are set for the weight in Draw() for the histograms). There will be 12 histograms created, 9 for the individual flavour/category files and 3 combined histograms, one for each flavour.

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