Style analysis is a form of constrained regression that uses a weighted combination of market indexes to replicate, as closely as possible, the historical return pattern of an investment portfolio. The resulting coefficients, called Sharpe style weights, are used to form inferences about a portfolio's behavior and composition. This technique has been widely adopted in the investment industry, despite the fact that no explicit confidence interval measures have been available to describe the results. We derive an approximation for the confidence intervals of these weights and, using Monte Carlo simulation, verify its efficacy. The estimation of these confidence intervals can help practitioners assess the statistical significance of their results and aids in determining which indexes to include in the analysis. It may also encourage the use of daily return data to meaningfully reduce the size of the confidence intervals.