Analyzing Experimental Results Effectively

The power of A/A testing A/A tests are easy to misunderstand. If both groups receive the same treatment, what is there to learn? Actually, a lot. A/A tests validate your experimentation system before you put commercial outcomes at risk. ✈️ This is week 4 of the series Experimentation in Airlines. A/A testing often surprises teams because it exposes issues long before treatment effects enter the picture. A/A tests help you learn two things: whether the system is biased and how noisy your world really is. A single positive uplift does not signal a problem. Repeated uplift across windows or replications could indicate bias. Wild swings are not failure but evidence that your metric is naturally volatile, which is exactly the insight needed for A/B runtime planning. ──────────────── One of the most practical uses of A/A testing: 👉 We use A/A tests to calculate historical variance, which we then feed into sample size and runtime planning for future A/B experiments. This is especially valuable in high-noise settings like airlines, where booking curves, spillovers, and disruptions create big swings in performance. The same principle applies across many industries: understand the natural variability before interpreting any uplift as real. Here is where this becomes real in airlines: ➡️ A/A tests can swing plus or minus ten percent even though nothing changed. Those swings reveal how noisy the environment is and they tell you two things: • 📉 Detecting real effects is inherently hard when the signal is much smaller than the noise. • 💡 Effect sizes that look small (like 1%) can still be commercially huge, even though they are buried inside large random fluctuations. So when A/A noise is ten times larger than the effect size you care about, naïve experimentation will simply not detect the real gains. If the natural noise in your metrics is an order of magnitude larger than the effect you hope to measure, you need more sophisticated designs to separate signal from noise. A/A tests also reveal whether your standard errors are correctly sized. With a true effect of zero, your rejection rate better not be much higher than alpha. If it is, your inference is miscalibrated before the real experiment even starts. ──────────────── The illustration below shows this clearly: even with no true effect, the estimated uplift can fluctuate widely, and variance reduction methods significantly tighten those distributions. Both curves are centered around zero, exactly what we expect in an A/A test. ➡️ More on variance reduction in future posts.✨ Does your industry show similar A/A volatility, or is it more stable? #ADCConsulting #AirlinePricing #CausalInference #Experimentation #RevenueManagement

Seven experts analyzed the same samples — and reported different particle sizes. This is not a problem with the instruments. It reflects a deeper issue in how we think about measurement. We are pleased to share our recent publication in AAPS Open: “Mastering Particle Size Analysis: Lessons, Challenges, and Future Directions from the FDA–CRCG Workshop.” https://lnkd.in/eBWFRUV4 The paper draws on a two-day FDA–CRCG workshop where scientists from regulatory agencies, industry, academia, and instrument manufacturers worked through shared datasets and real-world challenges in particle size analysis. A central insight emerged: Particle size is not a single, objective number. It is a result shaped by technique, assumptions, sample preparation, and—most critically—the purpose of the measurement. This has important implications. Much of the variability we observe across laboratories is not due to instrument performance, but to differences in methodological choices and interpretation. Moving forward, progress in the field will depend less on improving measurement precision alone, and more on: • clearly defining analytical intent • increasing methodological transparency • aligning on terminology and reporting expectations In that sense, particle size analysis is evolving—from generating numbers to enabling understanding. We hope this work contributes to ongoing efforts to strengthen consistency, interpretability, and regulatory confidence in particle size measurement. #CRCG #FDA #ParticleSize #PSD #AAPSOpen

How to understand that your statistical analysis might be garbage Sometimes significant results in omics don’t reflect underlying biology — they come from noise, suboptimal design, or misinterpretation. These common warning signs help you catch issues early. ✅ Sign 1: you have no biological replicates — only technical ones Technical replicates measure instrument variability. Biological replicates capture true system-level variability. Without biological replication, statistical significance becomes unreliable. ✅ Sign 2: your PCA clustering contradicts your experimental design If samples cluster by batch, processing date, or operator instead of biological groups, you’re primarily seeing batch effects rather than biological differences. ✅ Sign 3: you forgot about multiple testing Omics involves thousands of comparisons. Without FDR correction, the number of significant proteins is heavily inflated. ✅ Sign 4: you adjusted filters after seeing the results This falls under data snooping or p-hacking. Changing fold-change cutoffs, filtering rules, or removing outliers after inspecting the volcano plot can introduce bias. ✅ Sign 5: your results do not pass robustness checks If significance disappears: - under different filters, - after removing outliers, - after applying multiple testing correction, then the conclusions may not be stable. ✅ Sign 6: you have no independent biological validation If candidates are not supported by orthogonal methods, they remain hypotheses rather than confirmed findings. Statistics cannot compensate for poor design, missing replicates, or unaddressed batch effects. Building a solid analysis strategy before the experiment — and not relying on a bioinformatician to “fix it afterwards” — leads to far more reliable and interpretable omics results. #omics #proteomics #transcriptomics #bioinformatics #datascience #dataanalysis #analysis #massspectrometry #FDR #PCA #reproducibility #statistics #research #biology #researchdesign #phd

When I was interviewing users during a study on a new product design focused on comfort, I started to notice some variation in the feedback. Some users seemed quite satisfied, describing it as comfortable and easy to use. Others were more reserved, mentioning small discomforts or saying it didn’t quite feel right. Nothing extreme, but clearly not a uniform experience either. Curious to see how this played out in the larger dataset, I checked the comfort ratings. At first, the average looked perfectly middle-of-the-road. If I had stopped there, I might have just concluded the product was fine for most people. But when I plotted the distribution, the pattern became clearer. Instead of a single, neat peak around the average, the scores were split. There were clusters at both the high and low ends. A good number of people liked it, and another group didn’t, but the average made it all look neutral. That distribution plot gave me a much clearer picture of what was happening. It wasn’t that people felt lukewarm about the design. It was that we had two sets of reactions balancing each other out statistically. And that distinction mattered a lot when it came to next steps. We realized we needed to understand who those two groups were, what expectations or preferences might be influencing their experience, and how we could make the product more inclusive of both. To dig deeper, I ended up using a mixture model to formally identify the subgroups in the data. It confirmed what we were seeing visually, that the responses were likely coming from two different user populations. This kind of modeling is incredibly useful in UX, especially when your data suggests multiple experiences hidden within a single metric. It also matters because the statistical tests you choose depend heavily on your assumptions about the data. If you assume one unified population when there are actually two, your test results can be misleading, and you might miss important differences altogether. This is why checking the distribution is one of the most practical things you can do in UX research. Averages are helpful, but they can also hide important variability. When you visualize the data using a histogram or density plot, you start to see whether people are generally aligned in their experience or whether different patterns are emerging. You might find a long tail, a skew, or multiple peaks, all of which tell you something about how users are interacting with what you’ve designed. Most software can give you a basic histogram. If you’re using R or Python, you can generate one with just a line or two of code. The point is, before you report the average or jump into comparisons, take a moment to see the shape of your data. It helps you tell a more honest, more detailed story about what users are experiencing and why. And if the shape points to something more complex, like distinct user subgroups, methods like mixture modeling can give you a much more accurate and actionable analysis.

🔬 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗣𝗮𝗿𝘁𝗶𝗰𝗹𝗲 𝗦𝗶𝘇𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: 𝗟𝗲𝘀𝘀𝗼𝗻𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗙𝗗𝗔–𝗖𝗥𝗖𝗚 𝗪𝗼𝗿𝗸𝘀𝗵𝗼𝗽 🔬 Particle size distribution (PSD) measurement is fundamental to pharmaceutical development — yet what appears straightforward often conceals deep scientific complexity. In a recent meeting report by Xu et al. in 𝘈𝘈𝘗𝘚 𝘖𝘱𝘦𝘯, the outcomes of a two-day FDA–Center for Research on Complex Generics (CRCG) workshop on particle size analysis best practices are summarized, bringing together regulators, industry, academia, and instrument vendors. 📌 𝗧𝗵𝗲 𝘄𝗼𝗿𝗸𝘀𝗵𝗼𝗽 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗼𝗻 𝘁𝘄𝗼 𝗸𝗲𝘆 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: • 𝘋𝘺𝘯𝘢𝘮𝘪𝘤 𝘓𝘪𝘨𝘩𝘵 𝘚𝘤𝘢𝘵𝘵𝘦𝘳𝘪𝘯𝘨 (DLS) for nanometer-scale and colloidal systems • 𝘓𝘢𝘴𝘦𝘳 𝘋𝘪𝘧𝘧𝘳𝘢𝘤𝘵𝘪𝘰𝘯 (LD) for micron-scale suspensions and powders ⚠️ 𝗔 𝗰𝗲𝗻𝘁𝗿𝗮𝗹 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝗲𝗺𝗲𝗿𝗴𝗲𝗱: what analysts report as "size" depends heavily on model assumptions, sample preparation, and measurement purpose. Laboratories still report divergent results for identical materials — a gap with real consequences for development, technology transfer, and regulatory assessment. 🔍 Through vendor demonstrations and case studies on five pre-workshop materials (cyclosporine emulsion, iron sucrose, phytonadione, triamcinolone suspension, MCC), participants identified that 𝘁𝗵𝗲 𝗽𝗿𝗶𝗺𝗮𝗿𝘆 𝗱𝗿𝗶𝘃𝗲𝗿𝘀 𝗼𝗳 𝘃𝗮𝗿𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗿𝗲 𝗺𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹, 𝗻𝗼𝘁 𝗶𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁𝗮𝗹 — sample handling, dilution strategy, viscosity correction, and optical model selection being critical. 🤝 𝗞𝗲𝘆 𝗰𝗼𝗻𝘀𝗲𝗻𝘀𝘂𝘀 𝗽𝗼𝗶𝗻𝘁𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝗱: • Methodological transparency outweighs pursuit of a single "true" particle size • Fit-for-purpose analytical design is essential • Full-distribution analysis provides more insight than single-point metrics (D10, D50, D90) • Reference materials better representing real pharmaceutical systems are needed 🎯 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲-𝗮𝘄𝗮𝘆𝘀: • PSD data cannot be interpreted without understanding the measurement context • Sample preparation and handling are the dominant sources of inter-laboratory variability • Harmonized terminology, reporting templates, and validation criteria are urgently needed • A community white paper and inter-laboratory study were committed to as next steps 💡 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗯𝗿𝗶𝗻𝗴 𝘁𝗼 𝘁𝗵𝗲 𝗳𝗶𝗲𝗹𝗱? This workshop marks a collective shift toward treating particle size analysis as a discipline of interpretation, not just a technical exercise — with FDA and CRCG driving harmonization across the global pharmaceutical community. #ParticleSizeAnalysis #RegulatoryScience #PharmaceuticalDevelopment

Most analysts re-inject samples when results look abnormal. The assumption is simple: “If I inject again, the result may correct itself.” Sometimes it does. But repeated injections often hide the real issue. Re-injection may temporarily improve: ✓ peak shape ✓ %RSD ✓ response consistency But it does not answer the critical question: Why was the first injection different? Possible reasons include: ~ sample instability ~ carryover ~ incomplete mixing ~ system equilibration gaps ~ injection volume inconsistency If re-injection becomes a habit instead of an investigation tool, data starts looking stable without actually being reliable. Experienced analysts treat re-injection as a signal, not a solution. Before repeating injections, they ask: “What changed between the first and second result?” Reliability is not built by repeating measurements. It is built by understanding variation. Many research scholars learn this lesson only during thesis correction or audit discussions. #HPLC #MethodValidation

More than 70% of scientists fail to reproduce published studies, yet we benchmark against them. A Nature survey of nearly 1,600 researchers found that over 70% had tried and failed to reproduce another scientist’s work. While optimizing assays to achieve low variability is fundamental to scientific investigation, variability should not be confused with a lack of validity. Biology is inherently variable. Variability in in vivo assays is not necessarily indicative of a poorly performing test method. The challenge emerges when we look at how new methods are evaluated. In toxicology, animal studies are still treated as the reference standard. Yet their replicability varies widely depending on endpoint, study design, and classification approach. Binary outcomes behave differently from continuous ones. Mid-range classifications, often the most relevant for decisions, tend to be the least consistent. If in vivo studies are not fully replicable, expecting NAMs to exceed their precision may not be a realistic benchmark. Characterizing variability in traditional guideline studies can help establish more grounded expectations for NAMs. That is why this paper from Nicole Kleinstreuer’s group is worth reading: https://lnkd.in/e73use2c A more grounded approach may start with a simple step: quantify variability in existing methods.

Back when I was a Teaching Assistant (TA) at IITM, I was working on analysing academic performance data. I needed to estimate the average score of students across different departments, but collecting data from every student was impractical. So, I calculated averages using smaller samples. It seemed straightforward - until I realised something was off. Each sample gave me a slightly different average. How could I trust my estimate? That’s when I encountered the concept of the 𝗦𝗮𝗺𝗽𝗹𝗶𝗻𝗴 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗼𝗳 𝘁𝗵𝗲 𝗦𝗮𝗺𝗽𝗹𝗲 𝗠𝗲𝗮𝗻. It’s not just the average of a single sample that matters - it’s understanding how those averages behave when you repeatedly sample from the population. As a Machine Learning Engineer, I now see this concept in action almost every day. When building models, especially in cases like A/B testing or validating predictions, knowing the variability of sample means is crucial. This is where the 𝗖𝗲𝗻𝘁𝗿𝗮𝗹 𝗟𝗶𝗺𝗶𝘁 𝗧𝗵𝗲𝗼𝗿𝗲𝗺 shines: it tells us that no matter the population's distribution, the sample means will form a normal distribution (if the sample size is large enough). Why does this matter? 🤨 1️⃣ It helps you make accurate predictions from limited data. 2️⃣ It builds confidence in your estimates by quantifying uncertainty. 3️⃣ It’s the foundation for confidence intervals and hypothesis testing. In this video, I explain the Sampling Distribution of the Sample Mean in Hindi, breaking down this vital concept with simple, relatable examples. Whether you're a student preparing for exams or a professional solving data problems, this is knowledge you can’t skip. 👉 Watch the video here: https://lnkd.in/g-DgrYrd Let's learn this fundamental idea and level up your ML and statistics game! Because understanding variability is the key to reliable insights. 😊 FODO AI Want to learn Foundational Mathematics for Machine Learning in hindi ? Here is the complete playlist - 📽️ FODO Probability and Stats Playlist: https://lnkd.in/gi2pa778 📽️ FODO Linear Algebra Playlist : https://lnkd.in/gQh4RPNB #SamplingDistribution #MachineLearning #hindi #fodo #ai

Reproducibility is the unsung hero of intelligent materials discovery, because you can't optimize what you can't reproduce. That lesson resonates deeply with me. When working with real-world ingredients and complex chemical formulations, I've seen firsthand how hard it is to get consistent results due to ambient conditions, supplier batches, or human techniques. That variability doesn't just slow things down, it breaks the foundation for meaningful ML modeling. Automation holds real promise to resolve this. With standardized protocols and high-throughput capabilities, automated platforms improve consistency, compress timelines, and reduce exposure to ambient drifts. With sufficient high-quality data, we can move beyond batch-wise optimization, and instead model the full dataset and process systematically. A recent paper by Ian Marius Peters et al. exemplifies this. The team combined automated fabrication with hybrid machine learning search strategy to optimize perovskite solar cell preparation. Their approach included: 🔹Autonomous fabrication with SPINBOT: An automated fabrication platform that ensures consistent, high-throughput processing of solar cell samples 🔹Bayesian Optimization for global search: Exploring the complex multi-parameter space, identifying high-potential regions based on prior experimental feedback 🔹Umbrella method for local refinement: A gradient-informed search that zeroed in on stable, high-performance local maximums The outcome? High performance and low variation were consistently achieved. Additionally, ML-guided parameter search further reduced variability compared to human-experience-driven settings, likely by uncovering new stable, reproducible process windows. As we move toward intelligent, scalable materials discovery, let's double down on reproducibility, enabled by automation and engineered through ML-driven exploration. 📄 Hybrid Learning Enables Reproducible >24% Efficiency in Autonomously Fabricated Perovskites Solar Cells, Advanced Energy Materials, November 22, 2025 🔗 https://lnkd.in/emQV4dFA

#NIR Moisture Calibration in Combined Dairy Powders: A Tale of Lab Variability Working with nearly 3,500 samples of dairy powders, you get to see all sorts of colorful distribution patterns—and not the kind you'd hang on a wall. This graph shows just that—multiple distributions, each with its own quirks. But the dark blue one? It's the star of the show—tight, consistent, and looking sharp. Meanwhile, the bright orange distribution stands out for its variability, giving us a glimpse into the challenges of lab methods, techniques, and sample handling. Here’s the thing: even with a solid calibration approach, you can’t escape the differences between labs. The tightness of the dark blue distribution represents the precision we strive for, while the broader orange distribution reminds us that some variability is inevitable. So, what kind of error can you expect? That depends. The quality of your comparison lab is key. You can’t make up for the variability from lab practices, no matter how well-calibrated the system is. The best way to gauge this? Blind duplicates. They give you a real-world measure of lab-to-lab error, helping you understand whether what you’re seeing is the true variability, or just quirks in the lab setup. Just remember, your error isn’t just in the numbers—it’s in the lab doing the work. The real test? Blind duplicates. Because at the end of the day, even a perfect calibration doesn’t look so good if the comparison lab method is blah.