Pilot Testing Procedures

What is a Pilot BE Study? A Pilot BE Study is a preliminary clinical investigation conducted before the pivotal BE study. Its primary role is to assess whether the test formulation performs comparably to the reference product and to guide adjustments in formulation, dosing, or study design before committing to a larger, regulatory-submissible pivotal study. Objectives of Study The pilot BE study serves multiple purposes. It evaluates the feasibility of achieving bioequivalence between the test and reference formulations. It is also used to optimize the formulation or manufacturing process if initial results suggest improvements are necessary. Moreover, the pilot study helps assess intra-subject variability and residual error, which is critical for determining the required sample size for the pivotal study. It also assists in selecting the most appropriate dosage strength, especially when multiple strengths are available. Additionally, it helps estimate the test/reference (T/R) ratio to ensure it is likely to fall within acceptable regulatory limits, and to refine the blood sampling schedule to capture key pharmacokinetic parameters like Cmax, Tmax, and AUC. Key Features of Study Typically, a pilot BE study involves a small number of subjects—usually around 6 to 12. The results are not intended for regulatory submission but instead inform the design of the pivotal study. The study design is most often a 2-way or 3-way crossover, and it is usually conducted as an open-label study. While the pharmacokinetic parameters analyzed are the same as in a pivotal study, the primary purpose is to make a go/no-go decision for progressing to a full-scale BE study. Regulatory Notes Regulatory agencies such as the USFDA do not mandate pilot BE studies, but they are recommended—especially for high-risk, complex, or modified-release formulations.EMA takes a similar position and views pilot studies as useful tools for formulation optimization. In India, the CDSCO also permits and encourages pilot BE studies to support the planning of pivotal BE studies. When Are Pilot Studies Crucial? Pilot studies become particularly important in several scenarios. For new or complex formulations, they help confirm that the in vivo performance aligns with expectations. When high variability is expected in drug absorption, pilot studies help estimate this variability for better planning of the pivotal study. They are also essential for modified-release or narrow therapeutic index drugs, where precise absorption control is critical. Lastly, when it is the first time a generic formulation is being tested in humans, a pilot study provides foundational data to proceed confidently. Limitations Despite their value, pilot BE studies have limitations. They are not acceptable for regulatory filing due to the small sample size and exploratory nature. Furthermore, results from pilot studies may not always predict the outcomes of pivotal studies due to limited statistical power.

"This document describes the procedure used for a pilot of NIST’s Assessing Risks and Impacts of AI (ARIA) evaluation: ARIA 0.1. Subsequent reports will provide more detailed descriptions of the different ARIA 0.1 evaluation components. Five organizations participated, submitting a total of seven AI applications to be evaluated. In this document, we first describe the design of the three evaluation scenarios (TV Spoilers, Meal Planner, Pathfinder) and the three testing levels (model testing, red teaming, field testing). We then discuss the methods used for assessment via dialogue annotation and tester questionnaires. Finally, we describe our approach to measuring validity of AI applications using measurement trees. The pilot evaluation demonstrates the feasibility of a new approach to evaluation of AI systems: combining data from expert annotators and human testers, illustrated by a transparent measurement tool. " National Institute of Standards and Technology (NIST)

As part of the launch of the International Network of AI Safety Institutes, we conducted the Network's first-ever joint testing exercise, led by experts from U.S. AI Safety Institute, UK AISI, and Singapore AISI! The Network conducted this pilot exercise to explore methodological challenges, opportunities, and next steps for joint work that the Network can pursue to advance more robust and reproducible AI safety testing across languages, cultures, and contexts. The exercise was conducted on Meta’s Llama 3.1 405B to test across three topics - general academic knowledge, ‘closed-domain’ hallucinations, and multilingual capabilities. Here's what we learned: 🔍 Documentation is key. During the process, teams from the U.S., UK, and Singapore documented choices they made regarding testing parameters and prompting strategies, such as the level of randomness in sampling and limits on length of output. This documentation allowed Network Members to compare approaches and discuss the impact of methodological differences on results. The Network also tested the model by using two separate evaluation platforms – Moonshot and Inspect – to facilitate conversation on enhancing interoperability. 🔬 Small methodological differences can have a large impact. Minor differences in experimental design may impact test results, such as the choice of the precise benchmark implementation(s), model version(s), and model quantization(s) used; cloud hosting or hardware decisions; hyperparameters; modifications to prompts or agent design; and the methodology for scoring a model’s responses. For instance, all three AISIs used eight-shot prompting with Chain-of-Thought reasoning, and U.S. AISI and Singapore AISI used the same set of eight-shot examples, but results on GSM8K differed by more than 5 percentage points across the three AISIs 💡 Decisions about how much to augment tests to optimize model performance on benchmarks leads to variation in evaluation results. For example, when testing Llama 3.1-405B using the SQuAD2.0 benchmark, U.S. AISI, UK AISI, and SG AISI followed the same broad testing strategy but engineered prompts differently to explore how this impacted evaluation results: SG AISI and UK AISI did not use CoT reasoning and provided the model with basic instructions, whereas U.S. AISI used CoT reasoning and spent additional time optimizing prompts for SQuAD2.0 performance with prescriptive instructions. ➡️ This work will act as a pilot for a broader joint testing exercises leading into the AI Action Summit in Paris this February. The learnings from the pilot testing process will also lay the groundwork for future testing across international borders and evaluation best practices. Thanks to all of my colleagues at the U.S. AI Safety Institute that helped lead this work! This couldn't have happened without Gabriel M., Christina Knight, Tony Wang, Paul Christiano, Conrad Stosz, Mark Latonero, Andrew Kane, Vicki Ballagh, Elizabeth Kelly and so many others

The testing process typically follows a sequential order, starting with the least complex and moving toward the most complex and hardware-involved stages. Here's the usual order: 1️⃣MIL (Model-in-the-Loop) Testing: ✳️Done first to validate the model's logic and behavior in a simulated environment. ✳️Ensures the algorithm works as intended before any code is generated. 2️⃣SIL (Software-in-the-Loop) Testing: ✳️Conducted after MIL, once the model is converted into code. ✳️Validates that the generated code behaves the same as the model on a host machine. 3️⃣PIL (Processor-in-the-Loop) Testing: ✳️Performed after SIL, once the code is ready to be tested on the target processor. ✳️Ensures the code runs correctly on the actual hardware or an equivalent emulator. 4️⃣HIL (Hardware-in-the-Loop) Testing: ✳️Conducted last, after PIL, when the software is integrated with the actual hardware. ✳️Validates the full system in a real-time environment with simulated inputs/outputs. ⭕Summary of Order: MIL → SIL → PIL → HIL ✳️This sequence ensures that issues are caught early in the development process, reducing costs and risks as the system moves closer to deployment. Here's a brief overview of the differences between HIL, SIL, PIL, and MIL testing in the context of embedded systems and control software development: ✅MIL (Model-in-the-Loop) Testing: ⏩Tests the model of the system (e.g., Simulink) in a simulation environment. ⏩Focuses on verifying the algorithm and logic of the model. No actual code is generated or executed; it's purely simulation-based. ✅SIL (Software-in-the-Loop) Testing: ⏩Tests the generated code (e.g., C/C++) on a host machine (PC). ⏩Compares the behavior of the generated code with the model to ensure consistency. ⏩No hardware is involved; it validates the software functionality. ✅PIL (Processor-in-the-Loop) Testing: ⏩Tests the generated code on the target processor or an equivalent emulator. ⏩Validates that the code runs correctly on the actual hardware platform. ⏩Focuses on compiler optimizations, memory usage, and processor-specific behavior. ✅HIL (Hardware-in-the-Loop) Testing: ⏩Tests the entire system with real hardware components and simulated inputs/outputs. ⏩Validates the interaction between software, hardware, and the physical system. ⏩Used for final validation before deployment, often in real-time environments. Summary: ✔️MIL: Tests the model in simulation. ✔️SIL: Tests generated code on a host machine. ✔️PIL: Tests generated code on the target processor. ✔️HIL: Tests the full system with real hardware and simulated environments. Each step increases in complexity and hardware involvement, ensuring robustness at every stage of development. #automotive #iso26262 #sil #hil #pil #mil

How to Spot (and Fix) Manipulation Problems in Pilot Tests (Manipulation Checks Part 3) So you followed my advice. You designed a manipulation. You wrote a check. You pilot it. It doesn’t work. You’re baffled. Your advisor’s baffled. But you need it to work. Now what? Revisit the manipulation check. Pilot it again. Beat the weaknesses out early — before they cost you a full study. Spotting and fixing manipulation problems in pilots saves you time, protects your data, and keeps your research alive. Here’s how to catch issues early: 1. Look for Ceiling or Floor Effects If everyone scores super high or super low, your manipulation isn’t separating participants. How to fix it: Increase the contrast between conditions. If ceiling effects come from guessing, make the options less obvious. 2. Watch for Misunderstanding Participants may interpret your manipulation differently than you intended. How to fix it: Add open-ended questions like "What did you think the task was about?" Clarify any ambiguous instructions before you go live. 3. Check for Variability No spread in responses means no power to detect effects. How to fix it: Make conditions more extreme (without sacrificing realism). Use pre-screens to focus on participants likely to respond. 4. Look for Hidden Noise Sources Noise can drown out a working manipulation. Common sources: fatigue, device differences, distractions, ambiguous stimuli. How to fix it: Shorten tasks to keep attention. Standardize presentation formats across devices. Tighten the clarity and vividness of your stimuli. 5. Test for Emotional and Cognitive Impact Some manipulations need to feel different — not just be different. How to fix it: Add quick mood or engagement checks: "How interesting did you find the task?" 6. Know When to Scrap and Rebuild If tweaks don’t solve the problem, the design itself may be wrong. How to fix it: Return to the theory. Are you targeting the right psychological lever? Look at successful manipulations in prior studies and adapt from what already works. Remember, at the end of the day, if your pilot fails, it’s rarely random. Something in the design, the delivery, or the perception went sideways. Rigorous pilots are the foundation of good experiments. Bad pilots ignored are the death of your research. Catch problems early. Fix them hard. Or start over stronger. Best of luck. #ResearchMethods #PhDLife #ExperimentalDesign #AcademicWriting

I’ve now ran over 100 pilots (trials) at Gong. With a win rate of over 90%. 4 biggest lessons. 1. 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 Never begin a pilot without executive alignment. Ideally, the economic buyer has already been engaged through demos / the evaluation. If not, this is a great opportunity to get them looped in as a ‘give / get’ before starting. “Before approving a pilot, we require exec alignment. I’ve learned it’s much easier to ask for 20 minutes upfront and all be aligned, than 50K at the end. How can we loop ___ in?” 2. 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐂𝐫𝐢𝐭𝐞𝐫𝐢𝐚 Before beginning a pilot, align on success criteria with the team + economic buyer. Always come ready with criteria proposed to help guide them as to what they should be looking to prove. Keep them simple. Under promise, over deliver. I also use the time to uncover additional risk. “Say we nail all the success criteria, you love the pilot, but the team decides not to sign on (date). What are the most likely 2 reasons why?” 3. 𝐌𝐮𝐭𝐮𝐚𝐥 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐏𝐥𝐚𝐧𝐬 Create a mutual success plan that outlines the success crtieria, sessions, pilot resources, etc. and share it with your POC to encourage editing. I have 3 lines that include - security, legal, and signer. 4. 𝐒𝐜𝐡𝐞𝐝𝐮𝐥𝐞 𝐚𝐥𝐥 𝐬𝐞𝐬𝐬𝐢𝐨𝐧𝐬 𝐮𝐩𝐟𝐫𝐨𝐧𝐭 If your pilot / trial process includes trainings, insights, check-ins, get them scheduled in bulk. Never have to worry about grabbing a next meeting then. Key to all 4... having a great, repeatable template to guide the buyer. Snag my (free) mutual success plan: https://lnkd.in/gGDQKgfC 🦙🦙🦙

Your research design will fail. Not because you’re careless. Because real data is messy. During my PhD failure 2 almost ruined a publication. And your method only looks rigorous on paper. The fix is boring: Test assumptions early. Not all of them. The structural ones. The three failures that show up first: 1. Sample contamination    (your clean sample isn’t clean) 2. Hidden confounds    (the variable you ignored is the variable that wins) 3. Measurement drift    (your instrument changes while you’re not looking) ✅ Do this: Pilot with 10–20 participants before you commit. ❌ Not this: Build the full study, then hope the method holds. 🔴 Bad approach: Lock the design. Start collecting. Pray. 🟢 Good approach: Pilot fast. Break it on purpose. Fix it early. Here’s the exact checklist I use. Pre-registered Pilot Break-Test (5 steps): 1. Write the core claim in one sentence ↳ If you can’t, the method won’t save you. 2. List your top 5 assumptions ↳ Not nice to have. The ones that make results believable. 3. Stress-test three things first ↳ inter-rater reliability ↳ instrument validity ↳ exclusion criteria 4. Pre-register the pilot ↳ relentless detail beats false confidence. 5. Try to break the study ↳ if it survives your attack, it might survive reality. If you only do one thing: Stress-test measurement drift. If you want a method that survives: Test the parts that fail first. Because the most dangerous assumption is the one you never wrote down. This is the same approach I use to turn expertise into durable authority: Test the assumptions, then publish the framework. AI as a true thinking partner for this. Depth over hype. One email per week. 13,000+ experts. No fluff. → Profile link.

🔵 𝑪𝒐𝒎𝒎𝒐𝒏 𝑴𝒊𝒔𝒕𝒂𝒌𝒆𝒔 𝑹𝒆𝒔𝒆𝒂𝒓𝒄𝒉𝒆𝒓𝒔 𝑴𝒂𝒌𝒆 𝒂𝒏𝒅 𝑯𝒐𝒘 𝒕𝒐 𝑭𝒊𝒙 𝑻𝒉𝒆𝒎  #𝟱 - 𝗦𝗸𝗶𝗽𝗽𝗶𝗻𝗴 𝗣𝗶𝗹𝗼𝘁 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝗻 𝗦𝘂𝗿𝘃𝗲𝘆 𝗦𝘁𝘂𝗱𝗶𝗲𝘀 One of the most overlooked yet critical steps in survey-based research is pilot testing. Skipping this stage might save you time upfront but it often leads to serious consequences that compromise your entire study. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗰𝗮𝗻 𝗴𝗼 𝘄𝗿𝗼𝗻𝗴 ⬇️ → Respondents misinterpret your questions → Poor survey flow leads to drop-offs → Ambiguous items confuse participants → Data quality suffers—sometimes beyond repair → You spend hours cleaning flawed or inconsistent responses If you’ve ever launched a survey only to realize too late that questions weren’t clear or responses didn’t match your research objectives you’re not alone. But you can avoid this. 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗱𝗼 𝗶𝘁 𝗿𝗶𝗴𝗵𝘁 ⬇️ → Select a small group (5–10 people) from your target population → Ask them to complete your survey while noting: ↳ Confusing items ↳ Completion time ↳ Redundancy or gaps → Conduct brief follow-up interviews or debriefs → Revise the wording, logic, and structure of your survey accordingly → Document your process it’s excellent material for your methodology section 𝗣𝗿𝗼 𝗧𝗶𝗽: Even if your supervisor or journal doesn’t ask for it, pilot testing shows academic rigor. Reviewers love to see that your instrument has been refined through real-world feedback. Skipping pilot testing is like publishing a book without proofreading the draft. You can’t afford to miss this step if you want your results to be valid, credible, and publishable. 𝘐𝘧 𝘺𝘰𝘶'𝘳𝘦 𝘶𝘯𝘴𝘶𝘳𝘦 𝘩𝘰𝘸 𝘵𝘰 𝘥𝘦𝘴𝘪𝘨𝘯 𝘰𝘳 𝘷𝘢𝘭𝘪𝘥𝘢𝘵𝘦 𝘺𝘰𝘶𝘳 𝘴𝘶𝘳𝘷𝘦𝘺 𝘱𝘳𝘰𝘱𝘦𝘳𝘭𝘺, 𝘰𝘳 𝘤𝘰𝘯𝘧𝘶𝘴𝘦𝘥 𝘢𝘣𝘰𝘶𝘵 𝘩𝘰𝘸 𝘵𝘰 𝘥𝘰𝘤𝘶𝘮𝘦𝘯𝘵 𝘺𝘰𝘶𝘳 𝘱𝘪𝘭𝘰𝘵 𝘵𝘦𝘴𝘵𝘪𝘯𝘨 𝘱𝘳𝘰𝘤𝘦𝘴𝘴 𝘐'𝘮 𝘩𝘦𝘳𝘦 𝘵𝘰 𝘩𝘦𝘭𝘱. 𝘐 𝘰𝘧𝘧𝘦𝘳 𝘵𝘢𝘪𝘭𝘰𝘳𝘦𝘥 𝘨𝘶𝘪𝘥𝘢𝘯𝘤𝘦 𝘧𝘰𝘳 𝘳𝘦𝘴𝘦𝘢𝘳𝘤𝘩𝘦𝘳𝘴 𝘢𝘵 𝘢𝘭𝘭 𝘴𝘵𝘢𝘨𝘦𝘴. → 𝙁𝙚𝙚𝙡 𝙛𝙧𝙚𝙚 𝙩𝙤 𝙧𝙚𝙖𝙘𝙝 𝙤𝙪𝙩. #ResearchTips #SurveyDesign #PilotTesting #QuantitativeResearch #AcademicWriting #ResearchSupport #ResearchMethods #DataCollection #ResearchMistakes

When I started tarka, I had 0 customers and 100 problems. Today, we have a waitlist of 50 qualified customers and a "path" to product-market fit. Here's my 3-step strategy that made it possible: (This could well be your task list for the next 3+ months) Step #1 → Validate the value proposition Don't assume you know what customers want. Test it. Create at least 3 different value propositions for your idea. Then, reach out to potential customers and ask them to rate each one on a scale of 0-10. Go deeper: Ask them to rank the propositions together and explain their thinking. This gives you quantitative and qualitative data to work with. The highest-rated proposition becomes your focus. Step #2 → Create a pilot offer Forget about building a fully-fledged product. Start with a pilot. When we tested Tarka's concept, we created three different pilot versions: small, medium, and large. This allowed us to test price sensitivity and feature preferences. Pro tip: Include a 50% "pilot discount" for your first round. It incentivizes early adopters and gives you room to increase prices later, with the same users. You could also just grandfather them in. Step #3 → Convert them to a pilot When a potential customer shows interest, don't just say yes to everything. Dig deeper. Ask questions like: - "Why is that a requirement?" - "What about that is absolutely necessary?" - "Can we deliver that faster or slower?" These conversations help you design a pilot that truly takes care of a burning problem. Don't rush to create a perfect product. Learn as much as possible WHILE delivering real value. With these strategies, we turned Tarka from an idea into a waitlist of 50 qualified customers in just a few months. Your turn: Implement these steps. I promise you'll uncover insights you have never considered before. P.S. If you're struggling with identifying customer problems, check out my previous post on turning prospective customers into solvable problems.