1. A method comprising: receiving information about a subset of users of the social networking system, the information describing connections between users of the social networking system and actions taken by users on the social networking system; determining an income distribution model of the subset of users based on the received information; analyzing, by a computer processor, the income distribution model to normalize the received information; defining ranges of income brackets based upon the analysis of the income distribution model; determining, by a computer processor, one or more confidence metrics for the ranges of income brackets for a user of the subset of users, each confidence metric describing a likelihood that the user's income level falls within an associated income bracket; and determining a modifier for providing an advertisement to the user that is targeted to a particular income bracket based on the confidence metric associated with the particular income bracket.
1. A method comprising: receiving information about a subset of users of the social networking system, the information describing connections between users of the social networking system and actions taken by users on the social networking system; determining an income distribution model of the subset of users based on the received information; analyzing, by a computer processor, the income distribution model to normalize the received information; defining ranges of income brackets based upon the analysis of the income distribution model; determining, by a computer processor, one or more confidence metrics for the ranges of income brackets for a user of the subset of users, each confidence metric describing a likelihood that the user's income level falls within an associated income bracket; and determining a modifier for providing an advertisement to the user that is targeted to a particular income bracket based on the confidence metric associated with the particular income bracket. 8. The method of claim 1 , wherein analyzing the income distribution model to normalize the received information further comprises: selecting a distribution model from known distribution models based on a curve fit of the received information; determining a margin of error based on the curve fit of the selected distribution model; and identifying bad outliers in the selected distribution model.