Human factors in data science primarily focuses on understanding how human cognitive processes, biases, and behaviors impact the way data is collected, analyzed, and interpreted.

Confirmation bias refers to the tendency to seek out and interpret information that supports our existing beliefs, while ignoring or discounting information that contradicts them.

User experience design in data science aims to create data visualizations that are clear, concise, and easy for users to understand and interpret, regardless of their technical expertise.

Transparency in data science involves being open and honest about the methods, data sources, and limitations of a study, allowing others to scrutinize and replicate the findings.

The availability heuristic refers to the tendency to rely on information that is easily recalled or accessible, even if it may not be the most relevant or accurate.

The halo effect refers to the tendency to form an overall positive or negative impression of someone based on a single trait or characteristic, which then influences our perception of their other traits.

Cognitive dissonance refers to the psychological discomfort experienced when our beliefs or actions are inconsistent, which motivates us to adjust our beliefs or behaviors to reduce this discomfort.

Privacy in data science involves protecting the confidentiality and security of personal information, ensuring that it is not used or disclosed without the consent of the individuals concerned.

Framing bias refers to the tendency to make different judgments or decisions based on the way information is presented, even if the underlying data is the same.

The illusion of control refers to the tendency to overestimate our ability to control events, even when we have little or no actual control over them.

Recency bias refers to the tendency to believe that an event is more likely to occur because it has happened recently, even if there is no causal relationship between the two.

Justice in data science involves ensuring that the benefits of data science are distributed fairly and equitably across society, addressing issues of bias, discrimination, and access to data and technology.

The representativeness heuristic refers to the tendency to make judgments based on the similarity of two things, even if they are not necessarily related.

The sunk cost fallacy refers to the tendency to believe that an event is more likely to occur because we have already invested time and effort into it, even if the evidence suggests otherwise.

The availability heuristic refers to the tendency to believe that an event is more likely to occur because we can easily imagine it happening.