The first chapter contains an overview of what is accepted as good practice. We review several general ethical guidelines. These can be used to appreciate good research and to indicate where and how research does not adhere to them. Good practice is “what we all say we (should) adhere to.” In the second part of this chapter, the focus is more on specific ethical guidelines for statistical analysis. Of course, there is overlap with the more general guidelines, but there are also a few specifically relevant to statistics: Examples are misinterpreting p values and malpractice such as p hacking and harking.

This chapter uses a range of quotes and findings from the internet and the literature. The key premises of this chapter, which is illustrated with examples, are as follows. First, Big Data requires the use of algorithms. Second, algorithms can create misleading information. Third, algorithms can lead to destructive outcomes. But we should not forget that humans program algorithms. With Big Data come algorithms to run many and involved computations. We cannot oversee all these data ourselves, so we need the help of algorithms to make computations for us. We might label these algorithms as Artificial Intelligence, but this might suggest that they can do things on their own. They can run massive computations, but they need to be fed with data. And this feeding is usually done by us, by humans, and we also choose the algorithms to be used.

Writing the paper is one of the most challenging aspects of a project, and learning to write the report well is one of the most important skills to master for the success of the project and for sustaining a scholarly career. This chapter discusses challenges in writing and ways to overcome these challenges in the process of writing papers in the social and behavioral sciences. Two main principles emphasized are that writing is (a) a skill and (b) a form of communication. Skills are developed through instruction, modeling, and practice. In terms of communication, the research report can be conceived as a narrative that tells a story. Sections of the chapter focus on identifying common barriers to writing and ways to overcome them, developing a coherent and appropriate storyline, understanding the essential elements of a research paper, and valuing and incorporating feedback.

Research in consumer research has rightfully been criticized for p-hacking, hypothesizing after the results are known, and other practices that lead to overestimation of the reliability and replicability of published results. Remediation has centered on more closely approximating the ideal hypothetico-deductive (i.e., confirmatory) method. There has been a push toward forming, and registering, selective hypotheses before running experiments, testing only those hypotheses, and testing each hypothesis with a single, preplanned analysis. We argue that doing better confirmatory experiments is not the (whole) solution and that HARKing and running multiple analyses are not the problem per se. The problem is that we misrepresent exploratory research as confirmatory. Forcing exploratory research into a hypothetico-deductive straitjacket leads to bad hypothesis testing. The straitjacket also leads to bad exploration, crowding out essential, good exploration that deserves space in our journals. We propose a recipe for more honest consumer research, in which authors report exploratory studies meant to generate hypotheses followed by truly confirmatory studies that test those hypotheses.