How to Minimize Variation and Achieve Reproducibility (2023)

FAQs

How will you Minimise variables to ensure reproducibility? ›

In thinking about at an experiment holistically, consistency is key to minimizing variation. When you repeat an experiment, doing everything exactly the same as the first time is crucial.

There are several approaches to accounting for variability within the experimental design. Holding factors constant by utilizing standard protocols, the same instrumentation and settings, the same operator, and so on can eliminate the sources of variability. Using blocking can reduce other sources of variability.

With low variability, one can forecast a population centered on sample data, while high variability makes it hard for predictions as it has less consistent values.

Improving substandard research practices—including poor study design, failure to report details, and inadequate data analysis—has the potential to improve reproducibility and replicability by ensuring that research is more rigorous, thoughtful, and dependable.

Overall, reproducibility in research is hindered by under-reporting of studies that yield results deemed disappointing or insignificant. University hiring and promotion criteria often emphasize publishing in high-impact journals and do not generally reward negative results.

To reduce participant variability, it is important to randomly assign participants to the experimental conditions.

There is an inverse relationship between sample size and standard error. In other words, as the sample size increases, the variability of sampling distribution decreases.

Variation is the enemy of quality. It can be partitioned between "common causes" and "assignable causes." Common cause variation exists in every process--it can be reduced by process improvement activities, but not eliminated. It is the variation that is inherent in a process that is operating as designed.

Measuring reproducibility is challenging, in Goodman's view, because different conceptions of reproducibility have different measures. However, many measures fall generally into the following four categories: (1) process (including key design elements), (2) replicability, (3) evidence, and (4) truth.

How do you explain reproducibility? ›

Reproducibility refers to the consistency of measurements. It is the extent to which a tool can produce the same result when used repeatedly under the same circumstances. Reproducibility is used interchangeably with the terms repeatability and reliability.

Due to the nature of science, you cannot be sure that the results are correct or will remain correct. When you ensure reproducibility, you provide transparency with your experiment and allow others to understand what was done; whether they will go on to reproduce the data or not.

Variability Reduction is a multi-part strategy to reduce product variation and make a product more robust or fit to use, e.g., meet its performance requirements regardless of variation.

Standard error and standard deviation are both measures of variability. The standard deviation reflects variability within a sample, while the standard error estimates the variability across samples of a population.

Product quality improves customer satisfaction. Reducing variation in both key product features and manufacturing processes is a primary means of decreasing product defects and improving product quality. In turn, improved product quality may lead to decreased production costs.

Reproducibility and Generalization - A Cautious Approach

For example, a psychologist who found that aggression in children under the age of five increased if they watched violent TV, could generalize that all children under five would display the same condition.

Reproducibility depends only on whether the methods of the computational analysis were transparently and accurately reported and whether that data, code, or other materials were used to reproduce the original results.

A major cause is publication bias, where studies can become statistically skewed in the chase for significant results and overwhelming the correct results. Additional causes include questionable data analysis practices such as researcher degrees of freedom, data dredging, and HARKing.

One way to reduce measurement reactivity is to do pilot testing and eliminate instruments that may have ceiling or floor effects (Becker, Roberts, & Voelmeck, 2003). Another commonly used method to minimize measurement reactivity is concealing the purpose of the study.

To reduce the variability due to causes other than those manipulated by the experimenter, relatively homogeneous experimental units are carefully selected. Random allocation of a treatment to an experimental unit helps insure that the measured results are due to the treatment, and not to another cause.

Which study design reduces variability? ›

Randomized Block Design

Because this design reduces variability and potential confounding, it produces a better estimate of treatment effects.

As a sample size increases, sample variance (variation between observations) increases but the variance of the sample mean (standard error) decreases and hence precision increases.

Reduce variation means that one sample will have similar characteristics to another sample. Reduce variation means that whenever you gather a sample all the samples will have the exact same sample mean. Since the population is always going to vary it is impossible to reduce variation between samples.

As the sample size increases the sampling distribution tends to become normal. That is the sampling distribution becomes leptokurtic in nature. It happens only because with the increasing sample size the variability decreases as the sampling distribution resembles the population to a great extent.

Genetic variation is caused by: mutation. random mating between organisms. random fertilization.

The goal within Six Sigma for process variation is defined specifically and quantified, and always to reduce the variation in the process to a degree that the probability of a particular defect occurring is less than six sigma or 3.4 defects per million opportunity.

Major causes of variation include mutations, gene flow, and sexual reproduction. DNA mutation causes genetic variation by altering the genes of individuals in a population. Gene flow leads to genetic variation as new individuals with different gene combinations migrate into a population.

To assess the repeatability and reproducibility, use a gage R&R study (Stat > Quality Tools > Gage Study). Repeatability is the variation due to the measurement device. It is the variation that is observed when the same operator measures the same part many times, using the same gage, under the same conditions.

The precision of a measurement is a measure of the reproducibility of a set of measurements. The significant figures displayed on an instrument are an indication of the precision of the instrument. The diagram below illustrates the difference between accuracy and precision.

One crucial step in creating reproducible projects is to ensure that documentation starts on day one. The documentation process should explain why certain choices were made, as well as a range of important details needed to successfully execute the project – what Philip Stark refers to as “reproducibility”.

What kind of sample reduces variability? ›

As discussed earlier, use of larger random samples decreases the sample-to-sample variability and increases our confidence that the sample estimates are closer to the population parameters.

The system of these factors affecting HRV can be divided into the following five categories: physiological and pathological factors, environmental factors, lifestyle factors, non-modifiable factors and effects. The direct interrelationships between these factors and HRV can be regrouped into an influence diagram.

The standard deviation is the most commonly used and the most important measure of variability. Standard deviation uses the mean of the distribution as a reference point and measures variability by considering the distance between each score and the mean.

Consequently, the standard deviation is the most widely used measure of variability.

The best way to manage variations is to understand them first. This means understanding what causes them, why they happen, and how to predict and control them. Once you understand the process well enough, you can take steps to prevent them from happening.

Quality Glossary Definition: Variation. The Law of Variation is defined as the difference between an ideal and an actual situation. Variation or variability is most often encountered as a change in data, expected outcomes, or slight changes in production quality.

Control Through Sampling

As discussed previously, random sampling is often the best approach to obtain a representative sample. Random sampling not only controls several extraneous variables, it also allows us to generalize to a given population (increases external validity).

How can reproducibility of data be tested? ›

The reproducibility of data acquisition can be measured by scanning the same person multiple times, aligning the data in 3D, and calculating standard deviations of anisotropy in each pixel. This obviously depends on SNR, scanning times, and pixel resolution.

Best practices for minimizing bias in experimental procedures, including: blinding; systematic random sampling; inclusion of positive and negative controls; and methods of quality control for reliability and reproducibility.

Through replication, you can see how and if any extraneous variables have affected your experiment and if they need to be made note of. Through replication, you are more likely to be able to identify the undesirable variables and then decrease or control their influence where possible.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization. In restriction, you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

The ideal way to minimize the effects of confounding is to conduct a large randomized clinical trial so that each subject has an equal chance of being assigned to any of the treatment options.

By using a consistent environment, experimental design, and randomization, researchers can minimize the effect that potential extraneous variables can have on experiment.

To reproduce original results, researchers must have access to the original data, protocols, and key research materials. Reproducibility in research is greatly hindered if there is lack of access to raw data and scientific methodologies.