Post-selection inference, i.e. model building and doing inference on the same data set where the inference does not account for the model building stage.

Either: Given a data set and no predetermined model, a model is built based on the patterns found in the data set.
Or: Given a data set and a model, one often finds that the model is inadequate. The model is adjusted based on the patterns found in the data set.
Then: The model is used for inference such as null hypothesis significance testing.
The problem: The inference cannot be taken at face value as it is conditional on the data set due to the model building stage. Unfortunately, this fact often gets neglected in practice.

Post-selection inference, i.e. model building and doing inference on the same data set where the inference does not account for the model building stage.

Either: Given a data set and no predetermined model, a model is built based on the patterns found in the data set.
Or: Given a data set and a model, the model is often found to be inadequate. The model is adjusted based on the patterns in the data set.
Then: The model is used for inference such as null hypothesis significance testing.
The problem: The inference cannot be taken at face value as it is conditional on the data set due to the model-building stage. Unfortunately, this fact often gets neglected in practice.

Post-selection inference, i.e. model building and doing inference on the same data set where the inference does not account for the model building stage.

Either: Given a data set and no predetermined model, a model is built based on the patterns found in the data set.  Or: Given a data set and a model, one often finds that the model is inadequate. It is adjusted based on the patterns found in the data set.  Then: The model is used for inference such as null hypothesis significance testing. The inference cannot be taken at face value as it is conditional on the data set due to the model building stage. Unfortunately, this fact often gets neglected in practice.

Post-selection inference, i.e. model building and doing inference on the same data set where the inference does not account for the model building stage.

Either: Given a data set and no predetermined model, a model is built based on the patterns found in the data set. 
Or: Given a data set and a model, one often finds that the model is inadequate. The model is adjusted based on the patterns found in the data set. 
Then: The model is used for inference such as null hypothesis significance testing.
The problem: The inference cannot be taken at face value as it is conditional on the data set due to the model building stage. Unfortunately, this fact often gets neglected in practice.