Add probabilities of events that cannot occur in the same outcome. The page traces how the inputs become union probability before rounding.
Mutually exclusive events have empty intersections. Because no outcome is counted twice, the probability of their union is the sum of their probabilities. Mutually Exclusive Events can also be compared with independent events.
The calculation treats event probabilities according to their labels, not as interchangeable values in mutually exclusive events.
The rule combines disjoint score ranges, categorical outcomes, and alternative failure modes that cannot coincide.
A sum above one proves that the inputs cannot all describe mutually exclusive events on one sample space. Different names do not guarantee disjoint events.
Save extra digits internally if union probability will become an input to another calculation.
Probabilities 0.12,0.25,0.18 sum to 0.55, so one of the three disjoint events occurs with probability 55%. This Mutually Exclusive Events example can be compared with overlapping sets.
State why simultaneous occurrence is impossible, check the shared sample space, and add the event probabilities.
Transfer the source entries one at a time under event probabilities, keeping any units visible in your notes. This page can validate numerical ranges, but only the reader can confirm that each entry represents the intended quantity.
Increasing any disjoint event enlarges the union by the same amount until the total reaches one. Trying a nearby input should follow this pattern; otherwise inspect the field assignment.
Attach units and the name union probability whenever the answer leaves this page. A rounded value is suitable for presentation, while retained working digits are safer for a dependent calculation.
Independent events may overlap, so adding them without an intersection correction is generally wrong. That neighboring measure needs its own setup rather than a relabeled answer.
Copying only the decimal discards the setup. Pair the union probability with event probabilities and the unit convention so another reader can reconstruct its meaning.
For a small Mutually Exclusive Events example using Event probabilities, solve once with the displayed formula and once through direct enumeration or a probability tree. Two methods that organize outcomes differently are less likely to share the same hidden assumption error.
For Mutually Exclusive Events, when the exact list becomes too large, retain a simpler identity as a check: complementary probabilities sum to one, a Pascal row is symmetric, and adjacent factorial results have a known ratio. State which identity was used beside the reported answer.
Only when at least one has probability zero.
No outcome appears twice.
The assumptions are inconsistent.