Evaluating data for high-stakes decisions: A guide

📘 The Essential Data Filtering Guide — Download for Free! Decluttering your evidence early will save time and money in document review. With modern data filtering and culling techniques, you can drastically reduce your initial data volumes. Learn how in our free eGuide. 🔗 https://bit.ly/3iselHp

If you’re building knowledge graphs, you’re building a mirrorworld of sorts, using the data you can collect and the data you have at hand, refined and articulated so that the results can be trusted. By contrast, plain RAG won’t be effective inside business units that simply don’t generate vast volumes of examples. More at https://lnkd.in/g_dDbwVC

Every time I read a stats paper, this is what I see: authors searching for correlations and making up stories. It is due to the invention of computers making data too accessible and analysis too easy. Stop doing statistics. Most work is like this example. 

Where did I put that piece of paper? When did I do that experiment? Where did I save that data file? If you frequently find yourself searching for a critical piece of information you jotted down as you performed your experiment, trying to remember when you did what, or scouring your hard drive to find the right file, it may be time to revisit how you keep your research notes. 👉 https://bit.ly/43Mxk4c

📊 Data that tells stories... At the latest 'Specialist View Live, Matthew Pover from The Specialist Works asked Emma Gibbons and Michael Buchholz from magic works how they make data look as good as they do. 👇 Watch their quick take on why simplicity wins!

When you're working with and analyzing data, there are many small mistakes you can make that become big down the line. So in this guide, Oyedele walks you through some common pitfalls and how to avoid them. You'll learn about bias, unclear inputs, misinterpreting results, workflow gaps, and more. https://lnkd.in/gTmuf2FC

The Science of Measurement | Episode 2 Incrementality tests can be resource-intensive and when they’re not designed well, the results can be misleading. Measurement is only useful if it supports confident action. And that starts with understanding how reliable your test results really are. In this episode, McCall Milligan, Head of Data Science at Fospha, walks through five questions every marketer should consider before running an experiment, including how you control for contamination, ensure clean execution, and choose a metric that reflects the behavior you want to measure. Because a test is only valuable when the findings are well-powered, thoughtfully interpreted, and genuinely helpful in guiding what you do next. Read the full blog: 5 questions to ask your incrementality-test provider before launching an experiment: https://hubs.ly/Q03ThjVH0

💻 Working with scRNA-seq data every day? In our latest blog, we share practical tips and tricks to streamline your analysis and improve efficiency. Read the full post here: https://lnkd.in/egHJQwUM

As the Single Cell Discoveries data team’s resident tips and tricks wizard, Sofia Drobysheva has pulled together a great set of terminal hacks for computational biologists and bioinformaticians. Learn how to: 🧠 Speed up navigation with custom aliases ⚙️ Edit configs and other files directly in the terminal with sed or vim 🌿 Automatically display your current Git branch in your prompt These are the small, everyday improvements that make bioinformatics workflows faster, cleaner, and more reproducible! A must-read if you spend your days in the terminal 👇 🔗 https://lnkd.in/egHJQwUM

Read a very apt little statement today: Something to the effect of "If it's worth writing down, it needs to be worth reading." Why log data that doesn't get reviewed and doesn't impact your decision-making process?