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The nonprofit sector has come to deliver the majority of state-funded social services in the United States. Citizens depend on nonprofit organizations for these services, and nonprofits depend on government for financial support. Scholars have begun to ask important questions about the political and civic implications of this new organizational configuration. These questions have direct ramifications for the anti-prison movement given the explosive growth of nonprofit prison reentry organizations in recent years. To see how such organizations may impact political engagement and social movements, this chapter turns its focus on the intricate dynamics of client-staff interactions. Leveraging a yearlong ethnography of a government-funded prison reentry organization, I describe how such organizations can be politically active and at the same time contribute to their clients' political pacification. Staff members engaged in political activities in surrogate representation of their clients. While staffers advocated on their behalf, clients learned to avoid politics and community life, accept injustices for what they are, and focus instead on individual rehabilitation. By closely studying what goes on within a nonprofit service provider, I illustrate the nonprofit organization's dual political role and its implications for social movements and political change.

This paper tests whether Bayesian A/B testing yields better decisions that traditional Neyman-Pearson hypothesis testing. It proposes a model and tests it using a large, multiyear Google Analytics (GA) dataset.

This paper is an empirical study. Competing A/B testing models were used to analyze a large, multiyear dataset of GA dataset for a firm that relies entirely on their website and online transactions for customer engagement and sales.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the intellectual property fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits. Frequentist A/B testing identified fraud in bounce rate at 5% significance, and bounces at 10% significance, but was unable to ascertain fraud at the standard significance cutoffs for scientific studies.

None within the scope of the research plan.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the IP fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits.

Bayesian A/B testing can derive economically meaningful statistics, whereas frequentist A/B testing only provide p-value’s whose meaning may be hard to grasp, and where misuse is widespread and has been a major topic in metascience. While misuse of p-values in scholarly articles may simply be grist for academic debate, the uncertainty surrounding the meaning of p-values in business analytics actually can cost firms money.

There is very little empirical research in e-commerce that uses Bayesian A/B testing. Almost all corporate testing is done via frequentist Neyman-Pearson methods.