Thread by @selcukorkmaz

🧵1/10: Systematic vs. Random Error 🎯

Welcome to this thread where we'll explore the differences between systematic and random error, two types of error that can impact the accuracy and precision of your data. Let's dive in! #Statistics #DataScience

🧵2/10: Error in Measurements 📏

In any measurement process, there's a possibility of errors occurring. Understanding the types of errors that can arise helps us to design experiments that minimize their impact and improve the quality of our results. #DataScience

🧵3/10: Systematic Error 📐

Systematic errors, or biases, are consistent and reproducible inaccuracies that occur in the same direction every time. These errors can be due to faulty equipment, incorrect calibration, or even observer bias. #DataScience

🧵4/10: Identifying Systematic Error 🔍

To identify systematic errors, compare your measurements to known values or perform repeated measurements. If the error consistently occurs in the same direction, it's likely a systematic error. #DataScience

🧵5/10: Correcting Systematic Error ⚙️

To correct systematic errors, identify the source of the error and take appropriate action, such as calibrating your equipment, using a different measuring technique, or providing better training for observers. #DataScience

🧵6/10: Random Error 🎲

Random errors are unpredictable fluctuations that occur in both directions (positive or negative) and vary between measurements. They can be due to factors such as instrument noise, environmental conditions, or human error. #DataScience

🧵7/10: Identifying Random Error 🔍

Random errors can be identified by analyzing the spread or dispersion of your data. If the data is scattered and there's no consistent pattern or direction, the errors are likely random. #DataScience

🧵8/10: Minimizing Random Error 🛡️

To minimize random errors, increase your sample size or take multiple measurements and average the results. This will help to cancel out the random fluctuations and improve the precision of your data. #DataScience

🧵9/10: Accuracy & Precision 🌟

•Accuracy refers to how close a measurement is to the true value.
•Precision refers to how close repeated measurements are to each other.
•Systematic errors impact accuracy, while random errors impact precision. #DataScience

🧵10/10: Understanding the differences between systematic & random errors helps improve the quality of your data. Minimize systematic errors by identifying and correcting their sources, and reduce random errors by increasing sample size or averaging multiple measurements.