CASRAI Dictionary

Tag: research methods

  • Double-Blind Studies and Bias Control

    A double-blind study is a controlled trial in which neither the participants nor the researchers who deliver the intervention and assess outcomes know who has been assigned to which group. By concealing allocation from both sides, the design neutralises the conscious and unconscious expectations that would otherwise distort behaviour, treatment and measurement, making it a cornerstone of unbiased and credible causal research.

    Blinding works alongside randomisation. Where randomisation balances groups at the start, blinding keeps them comparable thereafter by preventing knowledge of assignment from influencing what happens next. The two are complementary pillars of the randomised controlled trial, and a study that randomises well but fails to blind can still be undermined by the expectations of those involved.

    The biases that blinding controls

    Different biases threaten a study at different points in its lifecycle. Blinding and allied safeguards each target a specific threat.

    Bias Where it arises Primary safeguard
    Selection bias At group assignment Randomisation and allocation concealment
    Performance bias During the intervention Blinding of participants and care providers
    Detection bias At outcome measurement Blinding of outcome assessors
    Attrition bias From dropout and missing data Intention-to-treat analysis, follow-up

    Selection bias occurs when groups differ systematically before treatment begins; it is addressed not by blinding but by randomisation and concealment. Performance bias arises when one group receives different co-interventions or attention because their assignment is known. Detection bias creeps in when those measuring outcomes are influenced by knowing who received what — especially for subjective endpoints. Attrition bias emerges when dropout differs between groups, which is why retention and intention-to-treat analysis matter.

    Why each bias matters

    It is worth understanding why these biases are so damaging. Performance bias inflates or deflates an apparent effect because one group is, in practice, treated differently — perhaps receiving more attention, additional co-interventions or subtly different care — purely because their assignment is known. Detection bias is especially insidious with subjective outcomes such as pain, mood or function, where an assessor who knows the assignment may unconsciously rate the treatment group more favourably. Attrition bias distorts results when participants who drop out differ systematically between groups; if those doing poorly on one treatment leave more often, the survivors make that treatment look better than it is. Each bias, left unchecked, can manufacture an effect that is not real or hide one that is.

    Single, double and triple blind

    The number of “blinds” describes how many parties are kept unaware of assignment. In a single-blind design, participants do not know their group, but the researchers do — controlling expectation effects in participants while leaving performance and detection bias on the researcher side unaddressed. A double-blind design conceals assignment from both participants and the clinicians delivering and assessing care, the configuration most associated with rigorous trials. A triple-blind design extends concealment further, typically to the statisticians or committee analysing the data, so that interpretation cannot be skewed by knowledge of group identity. The more parties blinded, the more points in the study where bias is closed off — though additional blinding adds logistical cost and is not always feasible. Most well-conducted trials settle on double-blinding as the practical balance between rigour and feasibility, reserving triple-blinding for contexts where analytic interpretation is especially sensitive to expectation.

    How blinding is achieved in practice

    Blinding is more than an intention; it requires concrete mechanisms. Identical-appearing treatments — matching tablets, capsules or infusions — keep participants unaware of their assignment, while a placebo provides an indistinguishable comparator. Coded packaging, central randomisation systems and independent statisticians who work with masked group labels extend concealment through delivery and analysis. Good trials also test whether blinding actually held, by asking participants and staff to guess their assignment; if guesses are better than chance, unblinding may have crept in and the results must be interpreted with that in mind. In drug trials, achieving an indistinguishable placebo can itself be a substantial design challenge, since taste, appearance and even the absence of expected side effects can betray which arm a participant is in. Where a perfect match is impossible, an active placebo that mimics minor side effects is sometimes used to preserve the masking.

    When blinding fails or breaks

    Blinding can be compromised even when well designed. Distinctive side effects can reveal which treatment a participant received; a dramatic clinical response can tip off an assessor; and emergencies sometimes require deliberate unblinding for safety. Each of these reintroduces the very biases blinding was meant to prevent. The mitigations are practical: rely on objective endpoints where possible, keep outcome assessors separate from those managing side effects, and document any unblinding so that readers can judge its likely effect. The aim is not perfection but transparency about how well masking was maintained.

    When blinding is impossible

    Some interventions cannot be hidden. Surgery, physiotherapy, dietary changes and many behavioural interventions are inherently visible to participants and providers. In these cases, researchers preserve as much rigour as possible by blinding the outcome assessors — particularly for subjective measures — and by using objective endpoints that are harder to influence. The placebo, a classic blinding tool, is discussed in our article on the placebo effect in controlled trials; where no convincing sham is feasible, transparency about the limitation becomes essential. These designs are common across the confirmatory studies described in our overview of the pharmaceutical R&D pipeline.

    Reporting and verification

    Readers can only judge a study’s protection against bias if blinding is reported clearly: who was blinded, how concealment was maintained, and whether it was successful. Reporting guidelines for trials ask authors to state explicitly which parties were masked and to flag any departures, precisely because vague phrases like “double-blind” are sometimes used loosely. This kind of methodological transparency, encouraged in our guidance for authors and across the research lifecycle, lets others assess and reuse the evidence with confidence. Documenting blinding alongside the standardised terminology in the CASRAI dictionary makes a trial’s safeguards legible to replicators and reviewers alike, rather than leaving them to be inferred.

    Frequently asked questions

    What is the difference between single and double blind?

    In a single-blind study only the participants are unaware of their group; in a double-blind study both the participants and the researchers delivering and assessing treatment are kept unaware, controlling a wider set of biases.

    Which bias does double blinding most directly address?

    Double blinding chiefly controls performance and detection bias — the distortions introduced when participants or assessors alter behaviour or judgement because they know who received the intervention.

    Can a study still be valid if blinding is impossible?

    Yes. Where the intervention cannot be masked, blinding the outcome assessors and using objective endpoints preserve much of the protection, provided the limitation is reported honestly.

    How does blinding relate to randomisation?

    Randomisation balances groups at the outset and counters selection bias; blinding keeps them comparable afterwards by preventing knowledge of assignment from influencing treatment and measurement. They work together.

  • What Is Psychology? Scope, Methods and the Scientific Discipline

    Psychology is the scientific study of mind and behaviour, using systematic observation, measurement and experiment to build and test theories. As an empirical discipline it spans the biological, cognitive, developmental, social and individual aspects of how people and animals perceive, think, feel and act. The American Psychological Association (APA) frames it as a science grounded in evidence rather than intuition or anecdote.

    The scope of the discipline

    Psychology sits at the intersection of the natural and social sciences. It draws on biology and neuroscience to understand the brain, on statistics to quantify behaviour, and on social science to study groups and culture. Its defining commitment is methodological: claims about the mind are evaluated against data gathered under controlled, reproducible conditions rather than accepted on authority. That commitment distinguishes scientific psychology from folk or popular psychology, which may offer intuitively appealing explanations that have never been tested. The discipline’s value lies in its willingness to discard attractive ideas when evidence contradicts them, and to quantify uncertainty rather than asserting confident conclusions about complex human behaviour.

    Major subfields

    Subfield Central question
    Cognitive psychology How do attention, memory, language and reasoning work?
    Developmental psychology How do mind and behaviour change across the lifespan?
    Social psychology How do others influence thought, feeling and action?
    Biological psychology How do brain and body underpin behaviour?
    Personality & individual differences How and why do people differ in stable ways?
    Clinical & counselling How are psychological difficulties understood and supported?

    Research methods

    Psychology relies on a toolkit of complementary methods. Experiments manipulate one variable while holding others constant to test cause and effect, ideally with random assignment to conditions. Observational and correlational studies measure variables as they naturally occur, describing associations without claiming causation. Psychometrics is the science of building and evaluating measures—questionnaires, ability tests and rating scales—so that scores are consistent and meaningful. Underpinning all of these is careful attention to reliability and validity, the twin pillars of sound measurement.

    Quantitative and qualitative approaches

    Psychological research is often divided into quantitative and qualitative traditions, and mature programmes frequently combine them. Quantitative work expresses phenomena as numbers and analyses them statistically, prioritising measurement, comparison and generalisation across large samples. Qualitative work—interviews, focus groups, thematic analysis of text—seeks rich, contextual understanding of how people make meaning, and is well suited to generating hypotheses or studying experiences that resist tidy quantification. Neither is inherently superior; the appropriate method depends on the question. A study estimating how common an attitude is needs quantitative survey methods, whereas one exploring why people hold that attitude may begin qualitatively. Mixed-methods designs deliberately pair the two so that numerical breadth and interpretive depth inform each other.

    The scientific method in psychology

    Psychological research follows the general cycle of the scientific method: observe a phenomenon, derive a testable hypothesis, design a study, collect and analyse data, and revise theory in light of results. Because human behaviour is variable, psychologists lean heavily on statistics to separate genuine effects from chance. The discipline has also become more reflective about its own methods following the replication crisis, adopting practices such as preregistration and data sharing to strengthen the reliability of published findings.

    Measurement and assessment

    Much of psychology depends on turning abstract constructs—intelligence, anxiety, conscientiousness—into numbers. This is harder than it looks, and the field has a long tradition of scrutinising its instruments. Popular tools are not automatically trustworthy: assessments such as the Myers-Briggs Type Indicator illustrate how an instrument can be widely used yet fall short on psychometric grounds. Responsible practice means reporting how a measure was validated, a discipline reflected in CASRAI’s work on responsible assessment.

    A short history of the discipline

    Psychology emerged as a distinct experimental science in the late nineteenth century, conventionally dated to Wilhelm Wundt’s establishment of a dedicated laboratory in Leipzig in 1879. Early schools—structuralism, functionalism and later behaviourism—debated whether psychology should study inner experience or only observable behaviour. The mid-twentieth-century cognitive revolution restored the study of mental processes such as memory and attention using rigorous experimental methods, and the subsequent rise of neuroscience linked those processes to brain function. This trajectory matters because it shows the field repeatedly tightening its methods, a self-correcting tendency that continues in today’s reforms.

    Statistics and inference

    Because behaviour varies between people and occasions, psychology cannot rely on single observations. It uses inferential statistics to ask whether a pattern in a sample is likely to hold in the wider population. Two ideas are central: effect size, which expresses how large a difference or relationship is, and statistical power, the probability that a study will detect a real effect if one exists. Underpowered studies—those with samples too small to reliably find the effects they seek—produce unstable, often exaggerated results. Understanding these concepts is essential to reading psychological research critically, and their neglect contributed directly to the field’s reproducibility problems.

    Distinguishing good evidence from popular myth

    A practical skill the discipline cultivates is separating well-supported findings from appealing but shaky claims. Many ideas that circulate as “psychology” in popular media—rigid personality types, single-study effects presented as laws, or memorable graphs taken at face value—rest on weaker foundations than their fame suggests. Sound practice asks how a finding was measured, whether it has replicated, and how large the effect actually is. This is why the field places such weight on reproducibility and on transparent reporting: a claim is only as good as the method behind it.

    Ethics in psychological research

    Because psychology studies people, it is bound by strong ethical standards. Core principles include informed consent, the right to withdraw, minimisation of harm, confidentiality and, where deception is unavoidable, careful debriefing. Institutional ethics committees, often called institutional review boards, review proposals before data collection begins, and professional bodies such as the APA publish detailed ethics codes. These safeguards became more formalised after historical cases in which participants were exposed to undue stress, and they now shape study design from the outset. Such governance is part of the wider research lifecycle that good metadata and clear terminology, recorded in resources like the research dictionary, are designed to support.

    Frequently asked questions

    Is psychology a science?

    Yes. Psychology uses the scientific method—systematic observation, hypothesis testing, controlled experiments and statistical analysis—to study mind and behaviour, and it revises its theories in light of replicable evidence.

    What are the main branches of psychology?

    Major subfields include cognitive, developmental, social, biological, personality and clinical psychology. They share common methods but differ in the questions they ask and the populations and processes they study.

    What methods do psychologists use?

    Psychologists use experiments, observational and correlational studies, and psychometric testing, supported by statistics. Method choice depends on whether the goal is to establish causation, describe associations or measure an attribute reliably.

    Why does measurement matter so much in psychology?

    Because psychological constructs are abstract, conclusions are only as good as the instruments used. Reliable, valid measures are essential, which is why the field scrutinises its tests and encourages transparent reporting for authors.

  • What Is Statistics? The Discipline and Its Role in Research

    Statistics is the discipline concerned with collecting, organising, analysing, interpreting and presenting data. At its core it is the science of reasoning under uncertainty: it provides methods for drawing conclusions about a whole population from a limited sample, and for quantifying how much confidence those conclusions deserve. Statistics underpins quantitative research across every field, from medicine and economics to ecology and the social sciences.

    Descriptive versus inferential statistics

    The discipline divides into two broad branches. Descriptive statistics summarise and describe the features of a dataset without claiming anything beyond it. Measures of central tendency such as the mean, median and mode, measures of spread such as the range and standard deviation, and visual summaries such as histograms all belong here. Descriptive statistics tell you what the data at hand look like.

    Inferential statistics go further: they use a sample to make estimates or test claims about a larger population that has not been fully observed. Estimation, hypothesis testing, confidence intervals and regression modelling are all inferential tools. The defining feature of inference is that it carries uncertainty, and statistics provides the machinery to measure that uncertainty rather than ignore it.

    Branch Purpose Typical tools
    Descriptive Summarise observed data Mean, median, standard deviation, charts
    Inferential Draw conclusions about a population Confidence intervals, hypothesis tests, regression

    Populations and samples

    The distinction between a population and a sample is fundamental. A population is the entire set of units a researcher wishes to understand: all adults in a country, every transaction in a year, all stars in a galaxy. A sample is a subset of that population actually measured. Because studying an entire population is usually impractical, researchers work from samples and infer to the whole. A numerical fact about a population is a parameter; the corresponding figure calculated from a sample is a statistic, and statistics as a discipline is largely the study of how well sample statistics estimate population parameters.

    Estimation and hypothesis testing

    Two complementary tasks dominate inferential work. Estimation asks how large a quantity is and how precisely we know it, producing point estimates and interval estimates such as confidence intervals. Hypothesis testing asks whether the data are compatible with a specific claim, typically a null hypothesis of no effect, and summarises that compatibility with measures such as p-values. Both rest on the idea that random sampling produces variation, and that this variation can be modelled probabilistically.

    Variability and probability

    Underlying all of statistics is the recognition that data vary. Two samples from the same population will rarely give identical results, and statistics describes this sampling variation using probability. Measures such as the standard deviation quantify spread within data, while probability distributions describe how estimates would behave across repeated sampling. This probabilistic foundation is what allows statisticians to attach honest measures of uncertainty to their conclusions.

    Why statistics is central to research

    Statistics is not an optional add-on to research; it shapes how studies are designed, how large samples need to be, how data are analysed and how findings are reported. Sound statistical practice is essential for reproducibility, because it disciplines researchers against over-interpreting noise and helps others judge whether a result is robust. Poor statistical practice, by contrast, is a recognised driver of irreproducible findings. CASRAI’s work on standardised reporting and the CASRAI dictionary supports clearer, more comparable statistical reporting across the scholarly record, and the reproducibility category tracks developments in this area.

    Frequently asked questions

    Is statistics a branch of mathematics?

    Statistics uses mathematics, particularly probability theory, but it is usually regarded as a distinct discipline. Its focus is on data, inference and the practical business of learning from observation under uncertainty, not on abstract mathematical structure alone.

    What is the difference between a parameter and a statistic?

    A parameter is a fixed numerical characteristic of a population, such as the population mean. A statistic is the corresponding figure computed from a sample, such as the sample mean. Statistics as a discipline studies how to estimate parameters from statistics.

    Why does statistics matter for reproducibility?

    Reproducibility depends on whether a reported result reflects a genuine effect or random variation. Statistical methods quantify that uncertainty and guard against over-claiming, so transparent statistical reporting is one foundation of a trustworthy scholarly record. See the CASRAI author guidance for reporting practices.

  • Randomised Controlled Trials: The Gold Standard Explained

    A randomised controlled trial (RCT) is an experimental study in which participants are allocated to an intervention group or a comparison group purely by chance, so that the only systematic difference between groups is the treatment under test. By combining randomisation, a control or comparison arm and, where possible, blinding, the RCT isolates the effect of an intervention from confounding factors, making it the methodological gold standard for answering causal questions.

    The core insight is simple but powerful: if allocation is genuinely random and groups are large enough, known and unknown confounders are distributed evenly across arms. Any difference in outcome can then be attributed to the intervention rather than to pre-existing differences between participants.

    Randomisation

    Randomisation is the process of assigning participants to groups by chance — for example, by computer-generated sequence. Its purpose is to balance characteristics such as age, severity and unmeasured risk factors across arms, removing selection bias from the comparison. Without it, sicker or healthier participants might cluster in one group, distorting the result.

    Allocation concealment

    Allocation concealment ensures that those enrolling participants cannot foresee or influence which group a person will join. It is distinct from blinding: concealment protects the randomisation process at the point of assignment, whereas blinding operates after assignment. Poor concealment is one of the most consistently demonstrated sources of exaggerated treatment effects.

    Control and comparison

    A control or comparison arm provides the counterfactual — what would have happened without the intervention. Comparators may be a placebo, standard care or an active alternative. The placebo arm in particular controls for expectation effects, a topic explored in our article on the placebo and placebo effect.

    Blinding

    Blinding (or masking) prevents participants, clinicians or assessors from knowing group assignment, reducing conscious and unconscious bias. The mechanics of single, double and triple blinding, and the specific biases they address, are set out in our companion guide to double-blind studies and bias control.

    Intention-to-treat analysis

    Intention-to-treat (ITT) analysis evaluates participants in the groups to which they were randomised, regardless of whether they completed the assigned treatment. This preserves the benefits of randomisation and gives a realistic estimate of effectiveness in practice, where adherence is imperfect. The contrasting per-protocol analysis, which includes only those who followed the protocol, can reintroduce bias and is usually treated as secondary.

    Why the RCT is the gold standard

    For causal questions about whether an intervention works, the RCT’s design controls the main threats to validity in one structure. It sits at the heart of the confirmatory stage of drug development, as described in our overview of the pharmaceutical R&D pipeline, and underpins evidence-based decision-making across the research lifecycle.

    Anatomy of a well-conducted RCT

    A robust trial weaves these elements together rather than relying on any single one. The table below summarises the core components and the threat each addresses.

    Component Purpose Threat addressed
    Randomisation Balance groups by chance Confounding, selection bias
    Allocation concealment Hide upcoming assignment Manipulation of enrolment
    Control arm Provide a counterfactual Mistaking change for effect
    Blinding Conceal group membership Performance and detection bias
    Intention-to-treat Analyse as randomised Attrition and post-hoc selection

    Power, sample size and pre-specification

    Randomisation only balances groups reliably when the sample is large enough, which is why trials specify a target sample size derived from the smallest difference worth detecting. Too small a study may miss a real effect or produce an unstable estimate; an adequately powered one gives the result interpretive weight. Equally important is pre-specifying the primary outcome and analysis plan before the data are seen, so that a single confirmatory test is fixed in advance rather than chosen afterwards. This connects directly to the practice of preregistration and Registered Reports, which protects the trial’s confirmatory status from later analytic flexibility.

    Where the RCT sits in the evidence hierarchy

    A single trial, however well conducted, is rarely the final word. Findings gain strength when they are replicated and when multiple RCTs are combined in systematic reviews and meta-analyses, which sit above the individual trial in the evidence hierarchy. Conversely, a well-designed observational study can sometimes be more informative than a flawed or under-powered RCT. The design is a powerful tool, not an automatic guarantee of truth, and its value depends on execution and transparent reporting.

    Internal versus external validity

    Two distinct questions decide whether a trial is useful. Internal validity asks whether the result is true for the participants studied — whether the design genuinely isolated the intervention’s effect from bias and confounding. External validity asks whether that result generalises to other people, settings and conditions. The RCT excels at the first: randomisation, concealment, control and blinding are precisely the tools that secure internal validity. It is weaker on the second, because the controlled conditions and selected participants that protect internal validity can make a trial less representative of routine practice. Strong evidence requires attention to both, and the two sometimes pull in opposite directions.

    Pragmatic versus explanatory trials

    This tension has produced two broad trial styles. Explanatory trials test whether an intervention can work under ideal, tightly controlled conditions — maximising internal validity and answering questions of efficacy. Pragmatic trials test whether it does work in everyday clinical settings with broader participants and fewer restrictions — favouring external validity and answering questions of effectiveness. Neither is superior in the abstract; the right choice depends on the question being asked. A regulator confirming a causal effect may want an explanatory design, while a health system deciding whether to adopt a treatment may learn more from a pragmatic one. Reporting which style a trial used helps readers interpret how far its findings should travel.

    Limits of the design

    RCTs are not universally applicable. They can be expensive, may exclude populations seen in routine practice, and are sometimes unethical or impractical — you cannot randomise people to harmful exposures. Tightly controlled conditions can also limit generalisability, the gap between efficacy (does it work in the trial?) and effectiveness (does it work in the real world?). Transparent reporting and good documentation, as encouraged in our guidance for authors, help readers judge how far a trial’s findings extend.

    Frequently asked questions

    What makes randomisation so important?

    Randomisation distributes both known and unknown confounders evenly across groups, so that observed differences in outcome can be attributed to the intervention rather than to pre-existing imbalances.

    How is allocation concealment different from blinding?

    Allocation concealment hides the upcoming assignment from those enrolling participants, protecting the randomisation itself. Blinding hides group membership after assignment to prevent biased behaviour and assessment.

    Why use intention-to-treat analysis?

    Analysing participants in their assigned groups preserves randomisation and gives a pragmatic estimate of effect under realistic adherence, avoiding bias introduced by excluding non-completers.

    When is an RCT not appropriate?

    When randomisation would be unethical, impractical or impossible — for example for harmful exposures or rare conditions — observational designs may be the only feasible option, accepting their greater vulnerability to confounding.