In one line: Bias is anything that pulls research away from the truth.
You already know bias. When you scroll your feed, you notice posts that fit what you already believe and skip the rest. Researchers are people too — so their expectations can quietly shape a study.
In psychology, bias is any influence that stops research being objective. It can slip in without anyone meaning it to.
Memory hook: Bias = a filter on the truth. It is not lying — it is an honest tilt that skews what we see or report.
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Key idea: Different biases sneak in at different stages. Learn to spot the main five.
Imagine a study asking: does a new app called StudyBoost improve grades? Watch how bias could creep in at each step.
Sampling bias
The people studied don't represent the wider group. Here: only students who already love apps volunteer, so the results won't apply to everyone.
Researcher bias
The researcher's hopes shape how they run or read the study. Here: they invented StudyBoost, so they may nudge students to use it well.
Participant bias
People act differently because they know they're being studied. Here: students try harder just because someone is watching.
Confirmation bias
You notice what fits your expectation and ignore what doesn't. Here: the researcher counts every grade rise but explains away the drops.
Publication bias
Mostly positive results get published. Here: 'StudyBoost works!' gets printed; ten studies where it did nothing never appear.
Exam tip: Name the type, then say how it would affect this study. 'There is bias' scores little; 'sampling bias means the results only apply to app-lovers' scores well.
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The concept behind it: Bias threatens objectivity — the whole point of research. If bias is not controlled, we cannot trust that the findings are really true.
This is why psychologists build in controls: standard instructions for everyone, a double-blind design, and checking their own thinking, called reflexivity.
Repeating a study (replication) is the biggest safeguard: if a biased result was a one-off, other researchers usually fail to repeat it.
Go further — higher-level insight: Publication bias hides the failures. Because journals prefer exciting 'it works' results, the studies where nothing happened sit unseen in a drawer — the file-drawer problem. So the published picture can look stronger than the truth.
How this is tested: Bias is one of the four named concepts for Paper 2 Section B (bias · causality · measurement · responsibility). There you are given a study and asked to discuss it with reference to a concept. Bias also frames Paper 1 Section C essays and Paper 3.
The skill: spot where bias enters, and explain how it weakens the study.
A researcher advertises for volunteers to test whether their new meditation app lowers stress. Volunteers rate their own stress before and after using the app for a week. The researcher reports that stress fell and concludes the app works. Discuss this study with reference to bias.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.
Common mistakes: 1. Just saying 'there is bias'. Always name the TYPE and its effect on the study.
2. Only listing biases. A [15] answer must weigh and reach a judgement.
3. Ignoring the concept. Keep the word 'bias' and objectivity central.