The multiplayer promise set disjointness is one of the most widely used problems from communication complexity in applications. In this problem there are $k$ players with subsets $S^1, \ldots, S^k$, each drawn from $\{1, 2, \ldots, n\}$, and we are promised that either the sets are (1) pairwise disjoint, or (2) there is a unique element $j$ occurring in all the sets, which are otherwise pairwise disjoint. The total communication of solving this problem with constant probability in the blackboard model is $Ω(n/k)$. We observe for most applications, it instead suffices to look at what we call the ``mostly'' set disjointness problem, which changes case (2) to say there is a unique element $j$ occurring in at least half of the sets, and the sets are otherwise disjoint. This change gives us a much simpler proof of an $Ω(n/k)$ randomized total communication lower bound, avoiding Hellinger distance and Poincare inequalities. Using this we show several new results for data streams: \begin{itemize} \item for $\ell_2$-Heavy Hitters, any $O(1)$-pass streaming algorithm in the insertion-only model for detecting if an $\eps$-$\ell_2$-heavy hitter exists requires $\min(\frac{1}{\eps^2}\log \frac{\eps^2n}δ, \frac{1}{\eps}n^{1/2})$ bits of memory, which is optimal up to a $\log n$ factor. For deterministic algorithms and constant $\eps$, this gives an $Ω(n^{1/2})$ lower bound, improving the prior $Ω(\log n)$ lower bound. We also obtain lower bounds for Zipfian distributions. \item for $\ell_p$-Estimation, $p > 2$, we show an $O(1)$-pass $Ω(n^{1-2/p} \log(1/δ))$ bit lower bound for outputting an $O(1)$-approximation with probability $1-δ$, in the insertion-only model. This is optimal, and the best previous lower bound was $Ω(n^{1-2/p} + \log(1/δ))$. \end{itemize}