Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...

Bias in Epidemiological Studies

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

Blind Procedures

Blind Procedures

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...

Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Addressing cultural and knowledge barriers to enable preclinical sex inclusive research.

eLife·2025

Same author

AZ14289671 is a highly selective and blood-brain barrier penetrant irreversible TKI that targets EGFRExon20 insertions.

Cell reports. Medicine·2025

Same author

A comprehensive welfare scoring system for graft versus host disease clinical assessment in humanised mouse models used for pharmaceutical research.

Frontiers in immunology·2025

Same author

Navigating the paradigm shift of sex inclusive preclinical research and lessons learnt.

Communications biology·2025

Same author

The Sex Inclusive Research Framework to address sex bias in preclinical research proposals.

Nature communications·2025

Same author

Bayesian modeling for analyzing heterogeneous response in preclinical mouse tumor models.

Science translational medicine·2024

Search research articles

Related Experiment Video

Updated: Jun 17, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

Preclinical pilot studies: Five common pitfalls and how to avoid them.

Natasha A Karp¹, Alan Sharpe², Benjamin Phillips¹

¹Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.

Laboratory Animals

|August 5, 2024

Summary

Pilot studies are crucial for effective research design but often suffer from poor planning and over-interpretation. Avoiding these common mistakes ensures reliable results for future experiments.

Keywords:

Pilots Sample size Sample size calculation

More Related Videos

Quantifying Cognitive Decrements Caused by Cranial Radiotherapy

Quantifying Cognitive Decrements Caused by Cranial Radiotherapy

Published on: October 18, 2011

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

Related Experiment Videos

Last Updated: Jun 17, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

Quantifying Cognitive Decrements Caused by Cranial Radiotherapy

Quantifying Cognitive Decrements Caused by Cranial Radiotherapy

Published on: October 18, 2011

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

Area of Science:

Biostatistics
Research Methodology
Experimental Design

Background:

Pilot studies are small-scale experiments to inform larger research designs.
Effective pilot studies are essential for the success of subsequent research.
Common errors in pilot studies can significantly limit their utility.

Purpose of the Study:

To identify and discuss five common mistakes in pilot study design and execution.
To provide practical guidance for avoiding these errors and enhancing pilot study effectiveness.
To emphasize the importance of proper planning and avoiding over-interpretation of pilot data.

Main Methods:

Statistical primer outlining common pitfalls in pilot studies.
Presentation of a simulation to demonstrate the impact of inaccurate variability estimates.
Guidance on best practices for pilot study implementation.

Main Results:

Insufficient planning and over-interpretation are key issues compromising pilot study utility.
Pilot studies often inaccurately estimate biological endpoint variability, frequently underestimating it.
Underestimation of variability can lead to inconclusive and unethical larger studies.

Conclusions:

Well-planned pilot studies are a vital component of the research cascade.
Avoiding common mistakes, particularly over-interpretation, is critical for maximizing pilot study effectiveness.
Implementing pilot studies to a high standard is necessary for robust scientific progression.