Common Biases In Business Research

In research, bias occurs when an error is introduced into sampling or testing which results in selecting or encouraging one outcome, conclusion, or answer over others. Bias can happen at any phase of research, including study design, methodology selection, data collection, and stating conclusions [1]. Given the significant threats of these biases on the reliability and validity of research conclusions, understanding different types of biases, their consequences, and treatment methods is the corner stone in avoiding such biases and an important step in critically evaluating research. This chapter discusses biases that are common in quantitative research, biases associated with quantitative research and biases that usually occur in quantitative research using qualitative data. It will focus on introducing business researchers to their definitions and sources. The chapter also suggests methods to uncover those biases and provides remedies and ways to deal with such biases

Epistemic Duty and Implicit Bias

In this chapter, we explore whether agents have an epistemic duty to eradicate implicit bias. Recent research shows that implicit biases are widespread and they have a wide variety of epistemic effects on our doxastic attitudes. First, we offer some examples and features of implicit biases. Second, we clarify what it means to have an epistemic duty, and discuss the kind of epistemic duties we might have regarding implicit bias. Third, we argue that we have an epistemic duty to eradicate implicit biases that have negative epistemic impact. Finally, we defend this view against the objection that we lack the relevant control over implicit bias that’s required for such a duty. We argue that we have a kind of reflective control over the implicit biases that we are duty-bound to eradicate. And since, as we show, we have this control over a wide variety of implicit biases, there are a lot of implicit biases that we have epistemic duties to eradicate

Biases in human behavior

The paper shows that biases in individual’s decision-making may result from the process of mental editing by which subjects produce a “representation” of the decision problem. During this process, individuals make systematic use of default classifications in order to reduce the short-term memory load and the complexity of symbolic manipulation. The result is the construction of an imperfect mental representation of the problem that nevertheless has the advantage of being simple, and yielding “satisficing” decisions. The imperfection origins in a trade-off that exists between the simplicity of representation of a strategy and his efficiency. To obtain simplicity, the strategy’s rules have to be memorized and represented with some degree of abstraction, that allow to drastically reduce their number. Raising the level of abstraction with which a strategy’s rule is represented, means to extend the domain of validity of the rule beyond the field in which the rule has been experimented, and may therefore induce to include unintentionally domains in which the rule is inefficient. Therefore the rise of errors in the mental representation of a problem may be the "natural" effect of the categorization and the identification of the building blocks of a strategy. The biases may be persistent and give rise to lock-in effect, in which individuals remain trapped in sub-optimal strategies, as it is proved by experimental results on stability of sub-optimal strategies in games like Target The Two. To understand why sub-optimal strategies, that embody errors, are locally stable, i.e. cannot be improved by small changes in the rules, it is considered Kauffman’ NK model, because, among other properties, it shows that if there are interdependencies among the rules of a system, than the system admits many sub-optimal solutions that are locally stable, i.e. cannot be improved by simple mutations. But the fitness function in NK model is a random one, while in our context it is more reasonable to define the fitness of a strategy as efficiency of the program. If we introduce this kind of fitness, then the stability properties of the NK model do not hold any longer: the paper shows that while the elementary statements of a strategy are interdependent, it is possible to achieve an optimal configuration of the strategy via mutations and in consequence the sub-optimal solutions are not locally stable under mutations. The paper therefore provides a different explanation of the existence and stability of suboptimal strategies, based on the difficulty to redefine the sub-problems that constitute the building blocks of the problem’s representation

Probabilistic biases meet the Bayesian brain

Bayesian cognitive science sees the mind as a spectacular probabilistic inference machine. But Judgment and Decision Making research has spent half a century uncovering how dramatically and systematically people depart from rational norms. This paper outlines recent research that opens up the possibility of an unexpected reconciliation. The key hypothesis is that the brain neither represents nor calculates with probabilities; but approximates probabilistic calculations through drawing samples from memory or mental simulation. Sampling models diverge from perfect probabilistic calculations in ways that capture many classic JDM findings, and offers the hope of an integrated explanation of classic heuristics and biases, including availability, representativeness, and anchoring and adjustment

Comment on "Biases in the Quasar Mass-Luminosity Plane"

Comment on "Biases in the Quasar Mass-Luminosity Plane"Comment: Comment on Biases in the Quasar Mass-Luminosity Plane; 3 page