Examples are measurements.

This is the same reference used in Technical mode. Expand it whenever you need the exact numbers, assumptions or further explanation.

Rows come from a collection process

A feature is input information such as time until an event. A label records a target such as whether the request is urgent. An observation is a recorded example. The sampling unit is the thing you intend to count or make claims about: a request, person, site or time period. These are not interchangeable.

The lab’s six fictional rows contain five request IDs because request A was copied into another export. Five requests still need not mean five people. One person could submit several requests. The sample is the observed collection; the population is the wider set you hope to describe. Neither the number of rows nor their neat appearance proves representative coverage.

Units change the meaning of a number

Row 2 records 180 minutes while other rows use hours. Adding these raw values produces a number with mixed units, not a meaningful average duration. Convert before comparing:

The conversion factor is one hour per sixty minutes. The minute units cancel, leaving hours. After conversion and removal of the known copied row, the observed durations are 2, 3, 48 and 24 hours. The missing duration remains unknown.

The numerator is a sum in hours and the denominator is the count of observed entries. This is the mean of the four observed durations, not automatically the mean for all five requests or for future requests.

A blank is not zero

Zero hours means the event is now. A missing deadline does not say that. If you insert zero, the arithmetic becomes 77 / 5 = 15.4 hours because you have invented a fifth observation. The workbench makes this consequence visible instead of treating filling as a harmless formatting choice.

Omitting a missing value is also a decision. If people with urgent or confusing requests are more likely to omit the deadline, the observed mean can be unrepresentative. Investigate how values go missing, document the policy and avoid unsupported conclusions. Later, when fitting preprocessing for a model, use training data only to estimate any filling values.

Repeated rows change weighting

Keeping the copied A row counts the two-hour request twice. After unit conversion, the mean becomes (2 + 3 + 48 + 2 + 24) / 5 = 15.8 hours. This difference is caused by extra weight on one request, not by new evidence about the population.

The lab identifies one exact copied export. In real data, similar feature values do not prove two records are duplicates. Different requests may happen to look identical, and repeated measurements may be intentional. Inspect identities and provenance before deleting or merging records. Group related observations appropriately when evaluating a model.

Label agreement is about the same examples

Annotators A and B label the same total number of requests urgent, yet disagree on rows 2 and 6. A total discards which requests received the label. Compare row-level labels and ask whether the target definition, available context or measurement process explains the difference.

More voting may make a procedure more consistent but does not automatically reveal a single objective truth. Document uncertain or disputed cases, preserve the original evidence and seek an appropriate resolution process. Repairing units cannot resolve disagreement about what “urgent” should mean.

The workbench is a measurement audit, not model training. Use it to distinguish units, missingness, repeated observations, label definitions and sampling coverage before moving to the data-quality and leakage lesson.