We study the problem of fairly allocating $m$ indivisible goods among $n$ agents who arrive online, under the notion of maximin share (MMS) fairness. Fair allocation with online arrivals is notoriously challenging: prior work achieves constant-factor MMS guarantees only when agents' preferences belong to a set of valuation functions known in advance, while no guarantees were known without such prior information. We develop a new randomized online algorithm for additive and submodular valuations, that we call Single-or-Sample, and that achieves a constant-factor approximation to MMS simultaneously for all agents, with constant probability. The algorithm requires no prior knowledge about the agents' valuations, and works against adversarial (oblivious) inputs. We further establish a fundamental tradeoff between approximation and success probability. Specifically, for any $c \ge 1$, no online algorithm can guarantee a $1/c$-approximation to MMS with probability exceeding $1 - 1/c^2$, even for binary additive valuations. This rules out constant MMS with probability asymptotically closer to $1$ than a constant. For XOS, the lower bound is much stronger: no algorithm can achieve even $1/\log\log n$-MMS to all agents with a constant probability. We complement this lower bound with an algorithm for the regime $c\in Ω(\log n)$, namely $1/c$-MMS to all agents with probability $(1-O(1/c))$, and show that this tradeoff is tight for XOS. Our constant-factor algorithm introduces several new ideas, combining greedy submodular maximization with randomized allocation and single-item reduction. A key technical ingredient is a new approach for analyzing iterative sampling without replacement. We develop concentration bounds that apply to a broad class of adaptive processes with complex dependencies across elements and rounds, which may be of independent interest.

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