Summary, Terminology and Practice (DP IB Theory of Knowledge): Revision Note

Scope

In the human sciences, knowledge is aimed at explaining and predicting patterns in human behaviour as individuals and in social groups. 

It is complicated by interacting factors like culture, language, bias, and personal history.

This means claims are usually provisional and context-dependent rather than universal laws.

Findings often generalise more reliably about groups and trends than about any one individual, and transferability across times and places is constrained because meanings and motivations can shift.

1) A school uses a well-being survey to spot year-group trends, but teachers avoid using the results to label what a particular student “must be feeling” without additional context.

2) A study finds a link between late-night phone use and tiredness in one city’s teenagers, but a different timetable and family routines in another region make the same prediction less dependable.

To what extent should human sciences prioritise explaining group-level patterns over predicting individual behaviour?

How does context-specificity affect what counts as a “valid” generalisation in the human sciences?

When, if ever, can knowledge from one culture or time period be transferred to another without losing its meaning?

Perspectives

In the human sciences, what researchers notice, measure, and treat as “normal” is shaped by pre-held starting assumptions about what people are like (for example, whether behaviour is mainly driven by individual choice, social structures or cultural meanings).

These starting viewpoints influence which questions are asked, how concepts are defined (such as “intelligence” or “wellbeing”), and what counts as an acceptable explanation.

This means different perspectives can produce different knowledge claims even from the same setting.

1) A researcher who assumes people are mainly self-interested designs a classroom study around rewards and penalties, while another who assumes people seek belonging focuses on group identity and peer approval in the same classroom.

2) When studying “stress,” one team begins from the assumption that stress is a biological response and frames it around hormones and symptoms, while another begins from the assumption that stress is culturally interpreted and frames it around expectations and social roles.

How do pre-held assumptions about human nature shape what counts as a worthwhile research question in the human sciences?

To what extent do different starting viewpoints create different “objects of knowledge” by defining key concepts in different ways? 

When perspectives conflict, what criteria (if any) can justify preferring one perspective over another in producing human sciences knowledge?

Methods and tools

In the human sciences, knowledge is produced and checked via the scientific method. 

Methods such as sampling participants, gathering data systematically and using replication, triangulation, and peer review to test whether findings hold up aid in enhancing reliability and validity.

These processes rely on tools like surveys and interviews, observations, statistical models and graphs, and shared language (definitions and categories) that make results communicable but can also simplify what they describe.

1) A class project compares sleep and concentration by using a short daily log (tool) and a consistent scoring rule for “concentration” (tool) so the method of comparison is fair across students.

2) A researcher studying friendship groups uses recorded interviews (tool) and a coding scheme (tool) so multiple researchers can apply the same method of thematic analysis and check if they reach similar interpretations.

How do the tools used to measure a concept influence what the concept comes to mean in human sciences knowledge?

To what extent should human sciences privilege methods that are easiest to replicate over those that capture richer detail?

How does the choice of sampling method shape what can legitimately be claimed from a study?

How important is precision when assessing the value of knowledge?

Ethics

In the human sciences, ethical responsibility shapes what knowledge should be pursued, how it should be gathered, and how it should be used, because studying people involves consent, privacy, and potential impacts on dignity and well-being.

Power differences between researchers, institutions, and participants raise issues of fairness and control, including who benefits from the knowledge, who is represented accurately, and who can challenge conclusions.

Ethical judgement also involves weighing likely harms and benefits when findings might influence policy, education or public attitudes, especially if results could be used to label, exclude, or disadvantage groups.

A researcher wants to use students’ learning-app data for a study, but they seek clear opt-in consent and give students a genuine choice without affecting grades, so participation is not pressured.

A local council pilots an “at-risk” indicator to target support, but an ethics review asks who can see the label, how mistakes can be corrected and how to prevent the data from being used to deny opportunities.

What ethical responsibilities do knowers have when human sciences findings could affect how people are treated by schools, employers, or governments?

How should the human sciences balance potential public benefit against risks to individuals’ privacy, autonomy, and dignity?

In what ways can power imbalances distort whose experiences become “knowledge,” and what would fairness require to address this?

Falsification

Testing the reliability of knowledge by proving some knowledge false.

Replication crisis

The inability to reproduce experiments to test knowledge and the ensuing concern about what this means for that knowledge

Participant observation

When the researcher (often an anthropologist) joins the group they are studying so as to observe from within the group

Blind testing

A method of gathering data by hiding key information from the participants to reduce the chance of bias

Causation vs correlation

Causation: X caused Y, Correlation: X happened, and Y happened; there may be a connection, but it is not causal

Post hoc ergo propter hoc

The fallacy of confusing a correlation with a causal connection 

Ethnocentrism

The judgment of another culture on the standards of one’s own culture

False positives

A test result that shows the presence of something inaccurately

Hypothesis

A proposed explanation or starting point, based on limited evidence that can be tested in an investigation

The observer effect

The tendency of people to behave differently when they are being observed