Reproducibility guarantees the same results of an experiment or study.
Reproducibility is the ability to repeat an experiment or study and obtain the same results. This implies that the procedures, data and analyzes used in the original study must be clearly documented and accessible for replication by other researchers under the same conditions. Reproducibility is a fundamental principle in scientific research, as it validates the reliability and validity of the findings.
Examples of reproducibility
We can find examples of reproducibility in different fields.
Natural sciences
- Biology : A study investigates the effect of a new medication. For results to be reproducible, others must be able to perform the same experiment with the same scientific methodology and obtain similar results;
- chemistry : An experiment to synthesize a new chemical compound must be reproducible. This means that other laboratories must be able to follow the same steps and conditions to obtain a compound with the same properties.
Physics
An experiment that measures the speed of light in different media must be reproducible. Other researchers should be able to use the same technique and obtain the same results, thus confirming the accuracy and validity of the study.
Math
A statistical analysis that concludes a significant correlation between two variables must be reproducible. Other researchers must be able to use the same data and analysis methods to reach the same conclusion.
social sciences
- Psychology : A study on the effects of stress on memory must be reproducible. Other researchers should be able to replicate the study with a similar sample and obtain comparable results;
- economy : an analysis of the effects of a fiscal policy on economic growth must be reproducible. Other economists must be able to apply the same models and methods to the same data to confirm the results.
Technology
An experiment on algorithms attempts to test the efficiency of a data compression algorithm. If it is reproducible, other researchers should be able to implement the same algorithm and obtain similar results in terms of compression and speed.
Medicine
A clinical trial testing the effectiveness of a new vaccine must be reproducible. Other researchers should be able to conduct trials with the same methodology in different populations and obtain similar results to confirm its efficacy and safety.
In biology, chemistry and medicine, reproducibility is also important for research.
Types of investigations
Experimental research
It focuses on determining cause and effect by manipulating independent variables and observing their effects on the dependent variables. Researchers control the conditions of the experiment to isolate the variable of interest and ensure that any changes in the dependent variable are a result of manipulation of the independent variable. This type of research is common in natural and social sciences, and is carried out in controlled environments such as laboratories.
Quantitative research
It is based on the collection and analysis of numerical data to identify patterns, establish relationships and make predictions. It uses statistical methods to analyze data and often employs surveys, questionnaires, and experimentation . This type of research is useful for generalizing results to a broader population, as it focuses on objectivity and precise measurement.
Qualitative research
It focuses on understanding phenomena, experiences and behaviors from a subjective and in-depth perspective. It uses methods such as interviews, focus groups, participant observation, and content analysis to collect non-numerical data, such as text, images, or recordings. This type of research is valuable for exploring complex contexts, understanding meaning, and gaining a deep understanding of human experiences.
Reproducibility and data
Empirical data
They are those obtained through observations, experiments or direct experiences. For a study to be reproducible, empirical data must be collected and documented in an accurate and detailed manner, allowing other researchers to replicate the study under the same conditions and obtain similar results.
Publication and data sharing
Data publication involves making the data used in a study available to the scientific community and the public. Data sharing refers to the practice of sharing data between researchers. Both are critical to reproducibility, allowing others to verify, reproduce, and extend the original findings. This encourages transparency and collaboration in scientific research.
Data privacy and security
Data privacy involves protecting the personal and sensitive information of research subjects, complying with data protection regulations and laws. Data security refers to the technical and administrative measures implemented to prevent data from being lost, manipulated or accessed without authorization. Both are crucial to maintaining trust in research and ensuring that data shared for reproducibility does not compromise privacy or security.
Data storage and management
Data storage involves storing it in a secure and organized manner, ensuring its long-term accessibility . Data management encompasses all practices related to its creation, storage, maintenance and distribution. Appropriate data management is essential for reproducibility, as it makes it easier for other researchers to locate and use the original data, ensuring that the data is accessible, understandable and usable in the future.
There are programming languages and programs for the study of statistics in relation to reproducibility.
Reproducibility and statistics
Statistical tools
Data analysis software, including programs and languages. To ensure reproducibility, it is important that the analyzes are documented and that the tools used are accessible to other researchers. Providing the code used allows others to accurately replicate the results.
Statistical inference
The process of using data from a sample to make generalizations or predictions about a larger population. For results to be reproducible, inference techniques must be clearly described and justified. This includes the selection of appropriate statistical models and the correct interpretation of the results, allowing other researchers to apply the same techniques to similar data and obtain comparable conclusions.
Statistical significance
Indicates the probability that the observed results are not due to chance. To replicate a study, it is crucial that the significance thresholds and statistical tests used are transparent. Results that are reproducible should show consistency in terms of statistical significance when replicated under the same experimental conditions.
Statistical power
The probability of detecting a true effect in a study, if one exists. A study with high statistical power is more likely to be reproducible, as it minimizes the possibility of obtaining false negative results. To ensure reproducibility, studies must be designed with an adequate sample size and sufficient statistical power so that true effects are detectable in the analyses.