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Randomized Experiments

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...

Blinding

Blinding

Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...

Group Design

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...

Controls in Experiments

Controls in Experiments

When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...

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Updated: Jul 12, 2026

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions

Published on: June 21, 2019

Randomized, Controlled, and Real.

Ali Al-Kassab-Córdova¹, Stephen Burgess², Nasir Bashir³

¹Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; Centro de Excelencia en Investigaciones Económicas y Sociales en Salud, Universidad San Ignacio de Loyola, Lima 150114, Peru.

Journal of Clinical Epidemiology

|July 9, 2026

Summary

This summary is machine-generated.

The term "real-world evidence" (RWE) misleadingly contrasts with randomized controlled trials (RCTs). Both study types observe reality; differences lie in methodology, not "realness," impacting causal inference and generalizability.

Keywords:

Epidemiology Randomized Controlled Trials Real-World Evidence Research Design Scientific Transparency

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Area of Science:

Clinical research methodology
Epidemiological study design

Background:

The term 'real-world evidence' (RWE) categorizes research by practice proximity, not methodology.
RWE is often derived from routine clinical data, implying greater 'real-life' relevance than randomized controlled trials (RCTs).

Purpose of the Study:

To critique the conceptual distinction between RWE and RCTs.
To argue that methodological structures, not perceived 'realness,' differentiate study types.
To clarify the concepts of external validity, representativeness, and generalizability in clinical research.

Main Methods:

Conceptual analysis of research terminology and classification.
Examination of methodological trade-offs in epidemiological study designs.
Critique of the 'real-world evidence' versus 'randomized controlled trial' dichotomy.

Main Results:

The distinction between RWE and RCTs is conceptually misleading; both are empirical.
Methodological structures, such as randomization, reduce error to isolate causal effects.
Representativeness does not equate to scientific validity; contextual applicability is key for inference.

Conclusions:

Randomized trials, observational studies, and quasi-experimental designs differ in methodological dimensions, not epistemic categories.
RWE and RCTs should be evaluated based on explicit methodological principles, not rhetorical labels.
Abandoning terms like RWE may foster greater conceptual clarity in evaluating clinical evidence.