Shadow Pulse 9

 William Gosset and the Revolution of Small Samples. 🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦 In the early 20th century, the world of statistics was dominated by one assumption: large numbers mattered(Nesselroade & Grimm, 2020). But for William Gosset, a chemist brewing ale at Guinness, small samples held untold insights. His revolutionary work on t-distribution and t-tests not only transformed statistics but opened the door to scientific advancements in countless fields. 📅📅📅📅📅📅📅📅📅📅📅📅📅📅 Imagine trying to determine the best barley for brewing with only a handful of samples. Traditional methods, relying on the z distribution and massive datasets were useless. Gosset realized the need for a new approach, one that could unveil the secrets hidden within small collections of data. 👀👀👀👀👀👀👀👀👀👀👀👀👀 His 1908 paper, "The Probable Error of a Mean," was a beacon in the statistical darkness. He recognized the limitations of the z curve and birt...

 Mean, Median, and Mode 🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰 Statistics is very intimidating to me and is kicking my ass this semester, so writing these blogs and relating them to something fun really helps me commit it to memory. So today I am introducing the Three Musketeers of Math: Mean, Median, and Mode. These swashbuckling statistics will help you understand any dataset like Zorro deciphers a secret message. 🌡️🌡️🌡️🌡️🌡️🌡️🌡️🌡️🌡️🌡️🌡️🌡️🌡️ Meet the Crew The Average Avenger : Mean is the sum of all the values of your data divided by the number of values. Think of it as sharing a pizza equally among your friends. Everyone gets a slice.  The Middle Mastermind : Median is the value that splits your data in half when ordered from least to greatest. Imagine lining up your friends by height. The median friend is smack dab in the middle, not the shortest or the tallest. The Most Popular Posse: Mode is the value that appears most often in your data. It'...

 A Superhero Guide to Independent and Dependent Variables 🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊 Ever dreamed of being a scientist, wielding potions and peering through microscopes? Let us embark on a scientific adventure, unraveling the secrets of independent and dependent variables, your super tools for understanding the world around you! Imagine a superhero lab Professor Potential who is our wise mentor, mixing bubbling concoctions and spouting scientific wisdom. Experiment X who is our trusty robot sidekick, ready for any test. The question is will Professor Potential's new super-strength serum actually work on experiment X The Key Players Independent Variable - aka The Twister: This is the variable we change or control in our experiment. Just like Professor Potential changing the formula of the serum, the independent variable gets twisted and turned to see its effect.\ Dependent Variable - aka the Detector: This is the variable we measure or observe to see how it...

 Investigating the Moon and Relapse with Statistics 🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪 Ever heard that full moons bring out the crazies? Or maybe just a few extra patients in the emergency room? While the image of howling werewolves might be exaggerated, the question of a lunar influence on human behavior persists. Today, we'll put on our lab coats and use statistics to investigate the fascinating and often debated connection between moon phases and relapse rates. 🌡️🌡️🌡️🌡️🌡️🌡️🌡️🌡️ The Suspect: Luna, the Earth's Satellite Lunar lore spans centuries, with beliefs linking the moon to everything from tides to fertility. But for our investigation, the focus is on potential changes in human physiology or psychology based on the moon's phases. Some theories suggest gravitational or electromagnetic forces might play a role, while others point to altered sleep patterns or increased suggestibility under a full moon. Gathering Evidence: The Power of Data To...

 Cracking the Case of Interval and Ratio Data!!! 🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊🧊 Remember the thrilling mystery novels where clues whisper secrets and numbers hold hidden truths? Well, buckle up, fellow data detective, because this time we are cracking the case of interval and ratio data! Gone are the days of simple yes or no answers - nominal data- or rankings without measurements - ordinal data. Now we are dealing with numbers that sing, dance, and reveal fascinating secrets about the world around us. Interval data  Imagine a thermometer. It displays degrees, from freezing cold to scorching hot, but there's no true zero. Zero on a thermometer does not mean the absence of heat, just some arbitrary starting point. That is interval data: numbers with equal differences, but no absolute reference point. Think of it like a ruler. Each centimeter is the same, but you would not say a book measured at zero centimeters is nonexistent. It just starts at a differe...

 Sorting it out: A Guide to Ordinal and Nominal Data 🪟🪟🪟🪟🪟🪟🪟🪟🪟🪟🧪🧪🧪🧪🧪🪟🪟🪟🪟🪟🪟🪟🪟🪟🪟🪟 Data, data, everywhere! Numbers dance across spreadsheets, charts bursting with colorful bars...but not all data is created equal. Ordinal and nominal, ever heard of them? Imagine a movie theatre: The seat numbers tell you where to sit, but there is no inherent order or comparison between them. You would not claim that one seat is better than the other. This is basically nominal data. Picture the rows: front row, middle row! The front row sits closer to the screen, the back row farther, and the middle row falls somewhere in between. Each level has a definite rank compared to the others. This is ordinal data. 🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪🧪 OK what is the difference? Nominal data: Think categories, not ranks. Imagine hair color, political party, or music genre. These are nominal-just labels that group things together without implying any order or inherent r...

Levels 🪟🪟🪟🪟🪟🪟🪟🪟🪟🪟🪟🪟🪟🪟🪟🪟🪟🪟 Four measurement scales - ways to categorize different types of variables and choose the right statistical test, visualization technique, and guide data analysis. nominal - names/ labels ordinal - order is important interval - space between/ tell us about order and the value between each item ratio - ultimate order, interval values, plus the ability to calculate ratios since a true zero can be defined Qualitative Data 🟥🟥🟥🟥🟥🟥🟥🟥 Nominal Variables - values are not ordered like nationality, gender, etc. Nominal scales are used for labeling variables without any quantitative value. They could simply be called labels nominal sounds like names and these scales are like names or labels. At this level, you can not do any quantitative mathematical operations like addition or division. You can do basic counts using pandas' value _counts method graphs like bar charts, and pie charts. Ordinal Variable s -  the order of the values is important...