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Related Concept Videos

Archival Research01:40

Archival Research

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Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
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Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Observational Studies01:11

Observational Studies

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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
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Cross-Sectional Research01:50

Cross-Sectional Research

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In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
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Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

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

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Body: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...
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Hindsight Biases01:12

Hindsight Biases

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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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Updated: Nov 20, 2025

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
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The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

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Reproducible Research: A Retrospective.

Roger D Peng1, Stephanie C Hicks1

  • 1Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland 21205, USA; email: rdpeng@jhu.edu, shicks19@jhu.edu.

Annual Review of Public Health
|January 20, 2021
PubMed
Summary
This summary is machine-generated.

Reproducible research ensures scientific findings can be recreated using original data and code. This review examines reproducibility in public health and proposes future improvements.

Keywords:
data analysisreplicabilityreproducibility

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Area of Science:

  • Computational science
  • Public health research
  • Data science

Background:

  • Computing advances enable large-scale data collection and complex analyses.
  • This leads to scientific discovery but raises concerns about reproducibility and replicability.
  • Reproducibility ensures independent verification of results using original data and code.

Purpose of the Study:

  • To review the origins and current status of reproducible research in public health.
  • To connect reproducibility with broader concerns about scientific findings' replicability.
  • To propose a path forward for enhancing both reproducibility and replicability.

Main Methods:

  • Literature review on reproducible research.
  • Analysis of the current state of reproducibility in public health.
  • Discussion connecting reproducibility and replicability.

Main Results:

  • Reproducibility is often hindered by the unavailability of original data and code.
  • Replicability concerns the confirmation of findings by independent studies.
  • Both are crucial for scientific progress but address different facets.

Conclusions:

  • Improving data and code availability is key to enhancing reproducibility.
  • Strengthening reproducibility and replicability is vital for public health research integrity.
  • A clear path forward is needed to bolster these principles in future research.