What do you do if your biotechnology work lacks scientific rigor?

I would add technical or biological replicates for increased sample sizes to increase statistical power and support. Determine whether the methods used are up to date, equipment is calibrated and proper negative and positive controls are applied in the experiments.

Seek feedback from peers or mentors in the field who can provide objective insights into areas that need improvement. Consider consulting with experts in statistics or experimental design to ensure your analyses are rigorous and appropriately interpreted. Document your process thoroughly, including any revisions made to your experimental protocols or analytical approaches, to demonstrate a commitment to scientific integrity and continuous improvement.

Reach out to mentors or supervisors who have experience in conducting rigorous biotechnological research. Their guidance and mentorship can be instrumental in helping you address any deficiencies in your work and develop a plan for improvement. Be open to receiving feedback with humility and a willingness to learn; constructive criticism is not a reflection of failure but rather an opportunity for growth. It's also beneficial to engage in discussions and debates within the scientific community, whether through conferences, seminars, or online forums, to gain diverse perspectives and refine your understanding of scientific principles.

Communicating with colleagues in research provides valuable opportunities to share knowledge and ideas, which can stimulate new experiments and even change the direction of your project. Regular discussion of results and methods can help you discover and correct potential errors and integrate new technologies, which speeds up the process and improves the quality of your research. Don't forget that constructive criticism and collaborative problem solving contribute to scientific progress.

Consider conducting pilot studies or validation experiments to test the effectiveness of your revised protocols before implementing them fully. These preliminary tests can help identify any potential issues or limitations early on, allowing you to make further adjustments as needed. It's essential to maintain detailed records throughout this process, documenting not only the changes made to your protocols but also the rationale behind these modifications. Transparency in protocol revisions is crucial for maintaining scientific integrity and facilitating reproducibility.

One of the limitations in sequencing protocols is the cost. At times it is not possible to redo a full sequencing run, only to add in some additional data. When possible it is most helpful to come up with a budget before approaching the project in order to make sure that the research question can get answered in the amount or type of sequencing available for that price. Are there ways to combine experiments to get the most out of the data from the sequencer?

Once you have gathered some insight and suggestions from peers, it is important to review and categorize those which are most likely to be impactful from those that will have less impact. Once you have sorted these, it is important to run validation trials to ensure that the suggestions are feasible and truly as impactful as you had thought. Then incorporate the validated suggestions into your protocol being sure to have enough replicates and controls to ensure that outcomes are statistically significant.

Consider employing robust statistical software or consulting with a statistician to ensure that your analyses are conducted accurately and effectively. Don't hesitate to revisit your data collection process to verify its integrity and reliability. Emphasize transparency by providing detailed descriptions of your analytical approach in your research publications or reports, allowing others to scrutinize and validate your findings.

Maintaining thorough documentation of changes ensures accountability and transparency throughout the research process. Include specific details such as the date of each modification, the rationale behind it, and any associated outcomes or results. This comprehensive record-keeping not only aids in internal quality control but also serves as a resource for addressing any inquiries or challenges regarding the integrity of your work. Consider implementing version control systems or electronic lab notebooks to streamline the documentation process and minimize the risk of errors or omissions.

Ensure that throughout your experimentation from initial concept through final results records are kept of every task that was performed, appropriate volumes and weights are recorded, and length of time at each step was recorded. I generally try to document in such a way as that, any peer with minimal knowledge of the project (given appropriate lab knowledge) can walk in and replicate my experiments from start to finish. If an SOP or other technical document is the end result of the experimentation, then I try to write it in such a way that an absolute novice with a minimum of relevant experience can be hired, and assume the role of performing the SOP with minimal training by following the SOP to the letter.

I think one of the biggest barriers to scientific rigor in biotech is fear. It’s scary to think your methods may be flawed or that your experiment doesn’t actually definitively say what you promised it would, especially if someone thinks they may get fired because of it. It’s important to confront such fears by making rigor a main part of organizational culture. Make careful measurements, continuous evaluation, transparency, and improvement part of your organization’s model. And let your people know it’s not just OK to bring up rigor-related issues, it’s encouraged.

You don’t publish it. And you don’t get grant money to study it. Simply put, you must design your experiments and/or trials to include the proper controls. You must use sample sizes determined by power analysis prior to initiating studies. And you must use current methods to measure/determine differences between groups. You must analyze data using stringent statistical methods.

Sometimes in molecular biology work, getting a statistically representative sample is less important than just getting one colony or individual reaction that worked. In those cases replicates are more important to increase chances of success than they are to apply statistics later.