Choose the counting model first: ordered selections of r distinct objects use n(n−1)⋯(n−r+1), while unordered subsets use C(n,r). A complete simple graph on n vertices has one edge per unordered vertex pair, giving n(n−1)/2 edges; loops and multiple edges would change the model. For n lines in general position in a plane, the kth line crosses the prior k−1 lines in distinct points and adds k regions. The total is 1+n(n+1)/2; parallels or triple concurrence invalidate that count.
Use complements to count at least one occurrence, and condition on the actual remaining population after a draw without replacement. For r iid outcomes chosen from n equally likely values, the probability that all are distinct is n(n−1)⋯(n−r+1)/n^r when r≤n. Its complement counts repeated outcomes. For a binomial count with N independent trials and fixed success probability p, mean is Np and variance Np(1−p). Identical probabilities alone do not establish independence.
A recurrence describes later values from earlier ones and needs enough initial data to determine a sequence. Separate the index from the value: a_n=2a_(n−1) with a_0=3 gives a_n=3·2^n. Graph and algorithm arguments often establish a recurrence by identifying what a new vertex or step adds. A closed formula should satisfy both the recurrence and its initial conditions; fitting a few observed terms does not prove it for every index.
Numerical approximation needs an error argument. Bisection preserves a sign-changing bracket for a continuous function and halves its width at each step; a zero may be absent if continuity fails. Newton’s update is x_new=x−f(x)/f′(x), requiring a nonzero derivative at the current point; convergence is not automatic from every starting value. An approximation’s residual and its error in x are different. State the method’s assumptions and a stopping criterion, rather than treating extra displayed decimals as accuracy.
