Use tree diagrams to map sequential decisions, compute path probabilities, and count conditional outcomes systematically.
Use tree diagrams to map sequential decisions, compute path probabilities, and count conditional outcomes systematically.
Review the core rule: Branch probability = product along the path
Always verify axis labels and legends before extracting values.
Check units on every axis before computing ratios or changes.
GI data requires interpolation — use approximate values and pick the closest answer.
All leaf probabilities sum to 1 — always identify what you're dividing by.
If group sizes differ, compute weighted average, not simple mean.
One counterexample makes a universal statement False.
Conclusions are limited to the data range provided, not broader populations.
Absolute change ($) and relative change (%) answer different questions.
P(at least one) = 1 − P(none). In set logic, use complement for "not" conditions.
A tree diagram shows two coin flips. What is the probability of getting Head then Tail?
A tree has three branches: Rain (60%), Cloudy (30%), Sunny (10%). Each day is independent. P(Rain both days) = ?
P(at least one head in 4 coin flips) = ?
A tree diagram for drawing 2 cards (no replacement) from a deck of 4 red, 6 blue cards. P(both red) = ?
A tree diagram has 3 levels. Each level has 2 branches with equal probability. The total number of end branches (leaves) is:
In a decision tree, Path A-B-C has probabilities 0.6, 0.4, 0.5. Probability of this specific path = ?
Sum of all leaf probabilities in a complete probability tree must be:
A tree shows: P(A) = 0.7, P(B|A) = 0.6, P(B|not A) = 0.3. P(A and B) = ?
P(B) using the total probability theorem, with P(A)=0.7, P(B|A)=0.6, P(B|not A)=0.3:
A tree diagram shows 3 paths to success. Their probabilities are 0.12, 0.08, and 0.15. P(success via any path) = ?
Multiply conditional probabilities from root to leaf.
Use this as a sanity check when building trees.
The complement is always faster for "at least one" scenarios.
Especially useful for sequential events with changing probabilities.