That was a simple example using independent events (each toss of a coin is independent of the previous toss), but tree diagrams are really wonderful for figuring out dependent events (where an event depends on what happens in the previous event) like this example:

Every branch or leaf stores the data for its entries in buffers of a size that can be specified during branch creation (default: 32000 bytes). How to Read a Tree builds on the joy that we take in trees—with some handy facts to go alongside that joy.”—BBC Radio 4

Books to support learning at home

In addition to the documentation in this manual, we recommend to take a look at the TTree tutorials: → Tree tutorials value=[23,20,25]’ describes the repartition of these irises among the tree possible classes of iris species, i.e. 23 for the setosa, 20 for the versicolor, and 25 for the virginica; While updating an existing tree is non-trivial, extending it with additional branches, potentially an “improved” version of an original branch, is straightforward. Reams of appealing facts make one itch to get outside and right up close to trees’ rough surfaces and shady cover.” The Atlantic This allows you to optimize read throughput for a given analysis, and is one of the main motivations for storing data in columnar format.

If the split level is TTree::kSplitCollectionOfPointers then the pointees will be written in split mode, possibly adding new branches as new polymorphic derived types are encountered. Filling a treeIf the split level is set to 0, there is no splitting: all data members are stored in the same branch.