  We argue that spanning large numbers of degrees of freedom in empirical
analysis allows better characterizations of effects and thus improves the
trustworthiness of conclusions. Our ideas are illustrated in three studies:
equity premium prediction, asset pricing anomalies and risk premia estimation.
In the first, we find that each additional degree of freedom in the protocol
expands the average range of $t$-statistics by at least 30%. In the second, we
show that resorting to forking paths instead of bootstrapping in multiple
testing raises the bar of significance for anomalies: at the 5% confidence
level, the threshold for bootstrapped statistics is 4.5, whereas with paths, it
is at least 8.2, a bar much higher than those currently used in the literature.
In our third application, we reveal the importance of particular steps in the
estimation of premia. In addition, we use paths to corroborate prior findings
in the three topics. We document heterogeneity in our ability to replicate
prior studies: some conclusions seem robust, others do not align with the paths
we were able to generate.
