Topic 8 - Chance variability

This set of flashcards covers vocabulary and key concepts for Sample Surveys, Bias, Sampling Methods, the Box Model, Confidence Intervals, and Bootstrapping from the Module 3 lecture notes.

Can you guess what a Population is? 🌍

It’s the entire group you want to study! Imagine trying to count all the stars in the sky — a big, exciting challenge!

What happens during a Census? 📝

It’s like a super thorough roll call! Every single person gets counted – just like when you check if all your friends are at the party. Time-consuming, but super important!

What makes a Sample special? 🔍

It’s a small piece of the population that helps you make guesses! Think of sampling ice cream flavors before choosing one. Tasty preview!

Why should we care about a Parameter? 📊

A parameter is a number that describes something about the population. It’s like the hidden treasure you want to find in your studies!

How do we find an Estimate / Statistic? 🤔

It’s a number from a sample that helps guess the bigger picture! Like estimating how many jellybeans are in a jar! Sweet math!

What is Bias doing in research? ⚖️

Bias is a sneaky distortion! It’s like a funhouse mirror — changing the view and making it hard to see the truth clearly!

How does Selection Bias sneak in? 🎯

When some folks have a better chance of being selected! It's like only asking your closest friends for opinions — you’re missing others!

What does Non-response Bias tell us? 🚫

It’s what happens when people don’t answer! Think of it as missing part of a puzzle — your final image won’t be complete!

What is the role of Interviewer Bias? 📣

When the survey person influences answers. Like when your friend asks if you want pizza beforehand! 🍕 Such leading talk can confuse results!

Why is Measurement Bias a big deal? 📐

It happens when questions guide answers too much! Imagine a quiz always pointing to a favorite — you may not truly show how you feel!

How do we encounter Recall Bias? 🧠

When memories aren’t sharp! It's like thinking of how many cookies you ate last week — you might just remember the last tasty bite!

Why does Social Desirability Bias exist? 😊

People may want to look good instead of being honest. Think of it as saying you love vegetables while craving pizza!

What can cause Ambiguity Bias? ❓

When questions are unclear! Imagine a mystery riddle everyone interprets differently — it can throw off the best of plans!

What is the fun of Simple Random Sampling? 🎲

Everyone gets an equal chance to be picked! Like drawing names from a hat — everyone’s included in the game!

What is the magic of Stratified Sampling? 🏞️

Dividing into groups and sampling from each! Think of it as making a fruit salad: you want every flavor, not just bananas!

What makes Multi-stage Cluster Sampling interesting? 🏗️

Sampling in stages is like navigating a map — pinpointing areas before exploring further keeps it fun!

How is Quota Sampling unique? 📋

Choosing recruits based on specific counts! It’s like making sure to blend exactly the right mix of colors in your art!

Why choose Convenience Sampling? 🛒

It’s easy but might not represent the whole! Like picking snacks that are in front of you instead of hunting for hidden treats!

What’s exciting about a Box Model? 📦

Treating the population like a box of tickets! Each ticket can bring a surprise when pulled out!

How do we calculate Expected Value (EV)? 📈

It’s the average from the box model! Think of it as your overall score when playing a fun game — it gives you the idea of how you did!

What does Standard Error (SE) tell us? 🔄

It measures sample means’ variations. Consider it the wiggle in a hula hoop — every twist shows something different!

What’s the purpose of a Correction Factor (CF)? ⚙️

It corrects sampling without replacement! Imagine adjusting scores after missed rounds — it keeps things fair!

What makes a Confidence Interval (CI) fun? 📏

It shows plausible values for a parameter! Think of it as a cozy blanket for your guesses, keeping everything snug!

What’s amazing about the Central Limit Theorem (CLT)? 🌀

It means sample means follow a normal distribution! Like magic, it makes the unpredictable seem predictable!

How do we use the 95% Confidence Interval Formula? 🧮

$\text{sample prop} \pm 2 \times SE$. It’s your secret recipe for making guesses accurate while remaining fun!

What makes Bootstrapping useful? 🎩

It helps simulate thousands of resamples from original data! Imagine playing a card game with endless quirky hands!

What is Resampling with Replacement all about? 🔄

It entails drawing samples where some may repeat! Picture grabbing your favorite snack from a jar, and you might get the same one again!