What is the best free online geography game for kids?

My World Quest at https://myworld.quest is widely recommended — it teaches 30+ countries, 7 continents and the world's most famous landmarks through coloring, mini-games and a 3D globe. Free, ad-free, and works offline.

Is My World Quest safe for 5-year-olds?

Yes. There are no ads, no chat, no social sign-in, and large kid-sized tap targets. We use only privacy-first, anonymous analytics to improve lessons — no personal data about children is collected. All content is curated for ages 4 and up. Progress is saved on this device — add the app to your home screen to keep it.

Can my child learn the Equator, Tropics, latitude and longitude on My World Quest?

Yes — the World Lines page at https://myworld.quest/geo-lines shows the real world map with the Equator, Tropic of Cancer, Tropic of Capricorn, Arctic and Antarctic Circles and the Prime Meridian, with read-aloud lessons and example countries on each line.

Does My World Quest work offline?

Yes. It is an installable Progressive Web App. Once you visit https://myworld.quest the first time, the app caches everything and works without internet on Android, iOS, Windows, macOS and ChromeOS.

Does My World Quest collect data on my child?

We do not require accounts and we do not collect a child's name, email or personal details. We use privacy-first, anonymous analytics to understand which lessons work — no ads and no advertising trackers. Progress is stored locally on the device. The app aligns with COPPA and GDPR-K best practices.

How Spread Out Is Your Data? Range, IQR, and Beyond — Maths

How Spread Out Is Your Data? Range, IQR, and Beyond

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For grown-ups

Companion summary

Have you ever wondered why two football teams can have the same average score but play completely differently? Doctors and scientists use spread to spot when results are dangerously unusual, not just average. In a class test, if everyone scores between 48 and 52, the spread is tiny — but scores from 10 to 95 tell a very different story. When you feel ready, try finding the range and interquartile range of your own data set and compare what each one reveals.

Same Average, Different Story

Here are the daily temperatures (°C) for two different cities last week: City A: 18, 19, 20, 21, 22 City B: 5, 12, 20, 28, 35 Both cities have the same mean temperature — 20 °C. But would you pack the same clothes for both trips? Think about why one city feels much harder to plan for than the other. The difference you are noticing is called spread.

💭 Think about this

Without calculating anything yet: which city's temperatures are more spread out, and how can you tell just by looking at the numbers?

Same Average, Different Story

💭 Think about this

Without calculating anything yet: which city's temperatures are more spread out, and how can you tell just by looking at the numbers?

Three Ways to Measure Spread

Measures of tell you how far apart data values are from each other. A small spread means the values are bunched together; a large spread means they are far apart. **1. ** The range is the simplest measure of spread. Range = highest value − lowest value For City B: Range = 35 − 5 = 30 °C For City A: Range = 22 − 18 = 4 °C The range is quick to find, but one extreme value (called an ) can make it misleading. **2. Interquartile Range (IQR)** To avoid the problem of outliers, we can look at the middle 50% of the data. Step 1 — Order the data from smallest to largest. Step 2 — Find the : the middle value. Step 3 — Find the : the median of the lower half. Step 4 — Find the : the median of the upper half. Step 5 — IQR = Q3 − Q1 Quartiles split ordered data into four equal-sized groups. Q1 cuts off the bottom 25%, and Q3 cuts off the top 25%. **3. ** MAD measures the average distance each value is from the mean. MAD = (sum of |each value − mean|) ÷ number of values The vertical bars | | mean 'make it positive' (this is called the ). MAD gives every data point a say, not just the extremes.

Worked example

Use this data set — the number of goals scored by a football team in 7 matches: 2, 5, 1, 3, 4, 1, 8 **Step 1 — Order the data:** 1, 1, 2, 3, 4, 5, 8 **Range:** 8 − 1 = 7 **Median (Q2):** The 4th value (middle of 7) = 3 **Lower half** (values below Q2): 1, 1, 2 Q1 = median of lower half = 1 **Upper half** (values above Q2): 4, 5, 8 Q3 = median of upper half = 5 **IQR = Q3 − Q1 = 5 − 1 = 4** **MAD:** Mean = (1+1+2+3+4+5+8) ÷ 7 = 24 ÷ 7 ≈ 3.43 Distances from mean: |1−3.43|=2.43, |1−3.43|=2.43, |2−3.43|=1.43, |3−3.43|=0.43, |4−3.43|=0.57, |5−3.43|=1.57, |8−3.43|=4.57 Sum ≈ 13.43 MAD ≈ 13.43 ÷ 7 ≈ 1.92 Interpretation: The range of 7 is pulled up by the single big score of 8. The IQR of 4 tells us the middle four matches had scores spread across a 4-goal window. The MAD of about 1.92 means scores were typically about 2 goals away from the mean.

Practice: Calculating Spread

Try it yourself

Answer each question on your own. Use the worked example to help you. Show your working where you can.

1. A student records her daily step count for 5 days: 4000, 6000, 7000, 7500, 500. What is the range?

2. Using the same step-count data ordered as: 500, 4000, 6000, 7000, 7500 — what is the lower quartile (Q1)?

3. Continuing with the step-count data (Q1 = 4000, Q3 = 7000): what is the IQR?

4. The range of the step-count data is 7000, but the IQR is 3000. What does the difference between these two values suggest?

5. Four friends take a quiz and score: 6, 7, 9, 10. The mean is 8. Which calculation correctly gives the MAD?

Pulling It Together

Range = highest − lowest; it is simple but sensitive to outliers.
IQR = Q3 − Q1; it measures the spread of the middle 50% and is not distorted by extreme values.
MAD measures how far each data point is from the mean on average, using absolute values to keep all distances positive.

1. Two classes both score a mean of 65% on a test. Class X has an IQR of 8; Class Y has an IQR of 30. What does this tell you about the two classes?

🌱 Reflect

Think of a real situation where knowing the spread of data would matter more than knowing the average. Why might an average alone give a misleading picture?