🎯 What it means: It's not enough to only know the "average" or central tendency (like mean) of data. Why? Because data can be clumped closely together or spread out like jam on toast. That’s why we have measures of dispersion!
📏 How to calculate
🤓 Why it's cool: SD considers ALL the values in the dataset. It's like giving every data point a VIP pass!
🌡️ Reading the SD
⚠️ Limitations
🍕 Real-world example: Imagine rating pizzas from different restaurants. If all restaurants have similar ratings (close to the average), SD is low. If ratings are all over the place, SD is high.
Dive deeper and gain exclusive access to premium files of Psychology HL. Subscribe now and get closer to that 45 🌟
🎯 What it means: It's not enough to only know the "average" or central tendency (like mean) of data. Why? Because data can be clumped closely together or spread out like jam on toast. That’s why we have measures of dispersion!
📏 How to calculate
🤓 Why it's cool: SD considers ALL the values in the dataset. It's like giving every data point a VIP pass!
🌡️ Reading the SD
⚠️ Limitations
🍕 Real-world example: Imagine rating pizzas from different restaurants. If all restaurants have similar ratings (close to the average), SD is low. If ratings are all over the place, SD is high.
Dive deeper and gain exclusive access to premium files of Psychology HL. Subscribe now and get closer to that 45 🌟