Lab Experiment Design

𝐁𝐞𝐟𝐨𝐫𝐞 𝐘𝐨𝐮 𝐁𝐞𝐠𝐢𝐧 𝐓𝐡𝐚𝐭 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭—𝐏𝐚𝐮𝐬𝐞. 𝐀𝐬𝐤 𝐖𝐡𝐲. Research is the foundation of any new invention, including new medicines. Researchers are the driving force behind progress, and not all researchers start with similar backgrounds. If you are a budding scientist considering a new experiment, please keep the following in mind. • What is the hypothesis? • Are the necessary controls in place? • Can I derive meaningful interpretations from the data? • If someone else shared this data with me, would I have confidence in it?   • Have similar experiments been conducted before? What lessons can I learn from those findings? • Do I fully understand the role of each reagent or formula used? This is essential for a thorough understanding of your experiment’s components. • Don’t conduct an experiment just to meet someone’s expectations—even your mentor’s. Pursue the truth instead. 𝗥𝗲𝘀𝗽𝗲𝗰𝘁 𝘁𝗵𝗲 𝘁𝗶𝗺𝗲, 𝗲𝗳𝗳𝗼𝗿𝘁, 𝗮𝗻𝗱 𝗳𝘂𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗴𝗼 𝗶𝗻𝘁𝗼 𝗲𝘃𝗲𝗿𝘆 𝗮𝘀𝘀𝗮𝘆.   • Make use of every available tool to meticulously assess your experimental design.    • Don't take for granted that you have plenty of time to fix potential issues. Treat each experiment as though it’s your last chance to achieve success. • Have 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 in your scientific abilities and in yourself. • Wishing you all the best on your journey to success!

Experimental design can feel daunting, especially if you don't have the support of senior scientists. That's why I created this breakdown to give you a seven-step guideline for establishing a project and bringing it to fruition. Let's get started 👇🏼 1. Formulate a clear research question ⤴ Define the specific problem or phenomenon you want to investigate. This will guide your entire experimental design. 2. Conduct a thorough literature review ⤴ Familiarize yourself with existing knowledge and research on the topic. This will help you identify gaps in understanding and avoid repeating previous work. Be extremely systematic in your approach. 3. Develop a testable hypothesis or a specific research question ⤴ Based on your general research question and literature review, formulate a clear, concise, and testable hypothesis that predicts the relationship between variables. If you select research question(s) instead of hypothesis, clearly define the specific problem or phenomenon you want to investigate. 4. Decide crucial details ⤴ Depending on your research question or hypothesis, determine independent and dependent variables, participants or data sources, sample size and replication, appropriate controls, experimental setup, etc. 5. Design your experiments / Develop data collection instruments ⤴ Outline the specific steps and variations of the independent variable and measure the dependent variable. Ensure that your procedure is clear, reproducible, and controls for confounding variables. If you deal with instruments, create or adapt existing tools for data collection (e.g., questionnaires, interview protocols, observational checklists). Ensure that your instruments are valid, reliable, and culturally appropriate. 6. Collect and analyze data / Interpret findings and draw conclusions ⤴ Gather data systematically and use appropriate qualitative or quantitative methods to analyze it. Interpret your results in the context of your hypothesis and existing literature. Dive deep into the implications of your findings and potential future research directions. 7. Observe the limitations and note future changes you need to make ⤴ Acknowledge the limitations of your study and take note of potential future research directions to build upon your findings and address any remaining questions. P.S.- Share your success with me, and let's celebrate together. _________ 🔔 This is Dr. Samira Hosseini. Scholars who took my training published +2,000 articles in top-tier journals. Join my inner circle not to miss even one single bit of learning: https://lnkd.in/eVNSihCM

From their abstract: This Perspective highlights common experimental design pitfalls and explains how to avoid them. We discuss principles of experimental design that are relevant for all biology research, along with special considerations for projects using -omics approaches. Established best practices for optimizing sample size, randomizing treatments, including positive and negative controls, and reducing noise (e.g., blocking and pooling) can empower researchers to conduct experiments that become useful contributions to the scientific record, even if they generate negative results.