Ever wonder why your data isn’t the same after repeating an experiment? Well part of science’s beauty lies in the difficulty of achieving reproducibility.
Heraclitus first said that no mans steps in the same river twice and the same can be applied to experiments. It is literally impossible to control for everything because the second time you do your experiment, you will be doing it at a different point in time. The good news is that most laboratory science is performed on such a small scale that a few days, weeks, even years between repeating an experiment wouldn’t affect your results. Let’s take a moment though to think about things that could.
Consistency is Key
Every step of an experiment, from set-up to collecting, processing, and running assays, requires dozens of steps, all of which are potential sources of variation. 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. This doesn’t mean you need to wear the same clothes and have the same tunes playing when repeating an experiment, but it does mean you need to take good notes in your lab notebook regarding how many micrograms of protein lysate was in how much total volume of PBS for that last immunoprecipitation. It’s impossible to know what kind of difference every little detail makes, but always assuming that everything and anything could affect your results will help you be consistent, minimize variation, and achieve reproducibility.
Everything’s Always Changing: Why Consistency isn’t as Easy as it Sounds!
It’s really hard to control for variables when by nature they are constantly changing! Take oxidation for example – it’s always occurring, so unless you have vacuum-sealed containers, beware. If you are treating cells with a solution that you pre-make by mixing a powder from the stock chemical cabinet with water, using the same solution you made up last time might seem like you are being consistent but due to oxidation(1-3) you might be working with a very different solution. Therefore make it up fresh each time!Ever leave a tube of water in the fridge or on the counter top untouched for a while? You’ll notice droplets accumulate inside the walls of the tube. This is because the universe is constantly in motion and every molecule on earth is subject to that motion. We use –80°C and –20°C freezers to slow things down but “stasis” on the nano-scale is fleeting. As a scientist, you’re constantly battling the eternal motion of the cosmos (deep right?) so the more consistently you deal with and control these battles, the more accurate and reproducible your results will be.
Good science is extremely difficult because minimizing variation means acting borderline-OCD. Once you get used to it however, it will make a world of difference and you’ll have data ready to publish. Here is a short list to get you started on thinking about ways to minimize variation. Use it to complement your own knowledge/experience in the lab, and when repeating experiments always remember, consistency is key.
Variables to Control for Consistent Results:
Re-used vs. Fresh ingredients
Let’s say you’ve extracted RNA, used some of it to make cDNA, froze the rest, and used all of the cDNA for q-PCR. Now you want to repeat the q-PCR run, so you go back to your frozen RNA. This adds variation because the first time around you made your cDNA from fresh RNA. Freezing and thawing your RNA may seem trivial but on the molecular scale it will likely have changed your RNA. Repeating the experiment from start to finish, performing each step as you did the first time will add a little more work in the short term, but will save you significant time and worries long term.
Speed and duration of spins can affect the final yield of your sample, which can subsequently have an effect on downstream applications due to variations in the concentration of reactants. Make sure that you are spinning at the same speeds – and remember RPM does not equal RCF so if you are switching between centrifuges make sure you calculate the correct speed!
The amount of “stuff” in your experiment, whether it’s for a treatment or a specific reaction, has a direct influence on your results, and will probably be the most significant contributor to variation. Keep volumes and concentrations the same for everything.
Three hours compared to two hours is a big difference. It’s mathematically 50% more time and in terms of molecules bathed in solution, it will allow many more interactions to take place. So before you run off for a long lunch think about whether it’s worth it if it messes up your results.
Frequency and duration of washes are both variables to consider. Not enough could decrease specificity and lead to artifacts in your data, while too many could wash away your molecule of interest.
For example, in molecular cloning, your insert:Vector ratio during ligation is critical to optimize. Once you’ve found the one that works, keep it the same.
Identical Products from Different Companies
Different manufacturers sell the same products, but they often differ in minor ways that can impact your data. For example, one cDNA synthesis kit uses random primers while another uses a mix of oligo dT and random primers. They both make cDNA, but the difference in specificity may have a significant impact on your data. If you need to switch companies for any reason check that the products are identical or as close to it as you can get. This include chemicals too,as purities and grades may differ between companies, so consider doing a side by side test before switching completely over to ensure there are no differences.
Room temperature is not an exact unit and can vary dramatically day-to-day depending on the season, whether the heating was turned up, or time of the day. Therefore instead of incubating reactions at room temperature, incubate them at a fixed temperature in a water bath or heat block.
Although your PCR machines all do PCR, they probably haven’t been used equally, and were manufactured at different times. These are factors that can eventually alter the efficiencies of machines. Using the same machine when repeating an experiment will add an additional level of control that could help tighten up your data.
One of the most common sources of variation is processing too many samples at one time. Not only could this affect results among independent experiments, but it could also affect results among samples within an experiment. so instead of trying to get too much done at once, take a breath and handle fewer samples. It may take you more time in the short run, but it could save you a lot of time in the future be preventing you from having to repeat a failed experiment.
These are my tips for ensuring consistency. Do you have any? If so please leave them in comments below.
- Calligaris, S., Manzocco, L., and Nicoli, M. C. (2007b). Modelling the temperature dependence of oxidation rate in water-in-oil emulsions stored at sub-zero temperatures. Food Chem. 101 : 1019-1024.
- Champion, D., Blond, G., and Simatos, D. (1997). Reaction rates at sub-zero temperatures in frozen sucrose solution: a diffusion controlled reaction. Cryo-Letters. 18: 251-260.
- Sudareva, NN. and Chubarova, EV., (2006). Time-dependent conversion of benzyl alcohol to benzaldehyde and bezoic acid in aqueous solutions. J Farm Biomed Anal. 41 (4) : 1380-1385
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Written by Ali Seyedali
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.How do you reduce variation in an experiment? ›
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.How do you make an experiment reproducible? ›
- Don't Read Between the Lines. ...
- Be Strict. ...
- Keep Things Transparent. ...
- Collaborate. ...
- Automate Your Processes. ...
- Addressing the Reproducibility Crisis.
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.How can reproducibility of results be improved? ›
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.What reduces the reproducibility of research? ›
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.How do you reduce participant variability? ›
To reduce participant variability, it is important to randomly assign participants to the experimental conditions.Does increasing sample size reduce variation? ›
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.Which cause of variation can be eliminated? ›
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.What are the measures of reproducibility? ›
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.
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.Why is reproducibility important and how can it be achieved? ›
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.What does it mean to reduce variability? ›
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.What are the two important measures of variability? ›
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.Why it is important that we reduce variances in the processes explain? ›
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.What is an example of reproducibility? ›
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.What causes reproducibility crisis? ›
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.How can researchers reduce or minimize reactivity in participants? ›
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.How does the experimental design reduce variability? ›
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.
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.What does it mean to reduce variation between samples? ›
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.Why does larger sample size reduce variability? ›
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.What are the 3 main causes of variation? ›
Genetic variation is caused by: mutation. random mating between organisms. random fertilization.How does Six Sigma reduce variation? ›
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.What are the 3 causes of variation? ›
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.How do you measure repeatability and reproducibility? ›
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.What measurement depends on its reproducibility? ›
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.Which step is most crucial to model reproducibility? ›
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”.
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.What factors influence variability? ›
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.What are 6 key sources of variation? ›
- There is wear and tear in a machine.
- Someone changes a process.
- A measurement mistake is made.
- The material quality or makeup varies.
- The environment changes.
- A person's work quality is unpredictable.
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.What is the best way to measure variability? ›
- Range: the difference between the highest and lowest values.
- Interquartile range: the range of the middle half of a distribution.
- Standard deviation: average distance from the mean.
- Variance: average of squared distances from the mean.
Consequently, the standard deviation is the most widely used measure of variability.How do you avoid variances? ›
- Adjusting your budget to be more realistic.
- Reconsidering your projected revenue by changing your prices, volumes or sales process.
- Increasing your customer demand by changing your product or increasing your marketing budget.
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.What is variation in quality improvement? ›
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.What is the most effective way to minimize the effect of all extraneous variables between the experimental group and the control group? ›
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).
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.Which method is best for minimizing bias in an experimental study? ›
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.How are scientists better able to eliminate unwanted variables from an experiment? ›
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.What technique do researchers use to reduce the impact of confounding variables? ›
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.How do you minimize 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.How can you minimize the effects of extraneous variables? ›
By using a consistent environment, experimental design, and randomization, researchers can minimize the effect that potential extraneous variables can have on experiment.What causes reproducibility? ›
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.