Questions should:

• Be clear and specific
• Have a limited number of answers
• Be easy to understand

Collecting Data

Ways to collect data:

Survey: Ask people questions

• "What's your favorite fruit?"
• Write down each answer

Observation: Watch and count

• How many cars pass by in 10 minutes?
• Count and record

Measurement: Measure things

• Height of each plant
• Temperature each day

Organizing Data with Tally Marks

Tally marks help you count as you collect data.

How tally marks work:

• | = 1
• || = 2
• ||| = 3
• |||| = 4
• |||| = 5 (cross through to make 5)

Example: Favorite Ice Cream Flavors

Benefits:

• Easy to mark quickly
• Groups of 5 are easy to count
• Can see totals at a glance

Creating a Frequency Table

Frequency table shows how often each value appears.

Example: Number of Siblings

From this table, we can see:

• Most students have 1 sibling
• Fewest students have 4 or more siblings
• Total students surveyed: 5+8+4+2+1 = 20

Creating Bar Graphs from Data

Steps to make a bar graph:

Step 1: Draw two lines (perpendicular) Step 2: Label the bottom (categories) Step 3: Label the side (numbers/scale) Step 4: Give it a title Step 5: Draw bars for each category Step 6: Check your work

Example: Pets Survey

Data: Dogs: 7, Cats: 5, Birds: 2, Fish: 4

Your bar graph should show:

    Number of Students
    |
  7 | ▓▓▓
  6 | ▓▓▓
  5 | ▓▓▓ ▓▓▓
  4 | ▓▓▓ ▓▓▓     ▓▓▓
  3 | ▓▓▓ ▓▓▓     ▓▓▓
  2 | ▓▓▓ ▓▓▓ ▓▓▓ ▓▓▓
  1 | ▓▓▓ ▓▓▓ ▓▓▓ ▓▓▓
  0 |____________________
      Dogs Cats Birds Fish
          Type of Pet

Creating Line Plots from Data

Steps to make a line plot:

Step 1: Draw a number line Step 2: Mark the scale (include all data values) Step 3: Label it Step 4: Add a title Step 5: Put an X for each data point Step 6: Stack Xs if values repeat

Example: Quiz Scores

Data: 7, 8, 9, 7, 10, 8, 9, 8, 10, 7

Line plot:

        X   X
    X   X   X   X
    X   X   X   X
   ─┼───┼───┼───┼──
    7   8   9   10
      Quiz Scores

Analyzing Data: Finding Patterns

Questions to ask about your data:

1. What is the mode? (Most common value)

• In quiz example: 8 appears 3 times (the most)
• Mode = 8

2. What is the range? (Spread of data)

• Highest value - Lowest value
• 10 - 7 = 3
• Range = 3

3. Are there clusters? (Groups of data)

• Most scores are between 7-9
• Only 2 scores of 10

4. Are there gaps? (Missing values)

• No one scored 6 or below
• No one scored between 8 and 9

5. Are there outliers? (Unusual values)

• Values far from the rest
• Example: If everyone scored 7-9 but one person scored 2

Making Conclusions from Data

Based on the quiz score data:

Observations:

• Most students scored 7, 8, or 9
• Two students got perfect scores (10)
• No one scored below 7
• The most common score was 8

Conclusions:

• The class did well overall
• The quiz might have been medium difficulty
• Students are understanding the material

Questions it might answer:

• Did students study? (Yes, scores are high)
• Was the test too hard? (No, many high scores)
• Do students need extra help? (Probably not, most scored well)

Real-World Data Projects

Classroom Projects:

1. Weather Watch

• Collect: Daily temperature for 2 weeks
• Graph: Line plot or bar graph
• Analyze: Warmest day? Range? Pattern?

2. Favorite Lunch

• Collect: Survey 20 students
• Graph: Bar graph of choices
• Analyze: Most popular? Least popular?

3. Reading Challenge

• Collect: Books read by each student
• Graph: Line plot
• Analyze: Average? Who read most?

Comparing Two Sets of Data

Example: Ice Cream Sales

Week 1:

• Monday: 15, Tuesday: 18, Wednesday: 20

Week 2:

• Monday: 12, Tuesday: 14, Wednesday: 16

Analysis:

• Week 1 had higher sales each day
• Both weeks showed sales increasing mid-week
• Difference between weeks: about 3-4 sales per day

Using Data to Make Predictions

If you see a pattern, you can predict:

Temperature data:

• Mon: 65°F, Tue: 68°F, Wed: 71°F, Thu: 74°F
• Pattern: Going up 3° each day
• Prediction: Friday might be 77°F

Book sales:

• Week 1: 10, Week 2: 15, Week 3: 20
• Pattern: Up 5 each week
• Prediction: Week 4 might sell 25 books

Note: Predictions are guesses based on patterns - they might not always be right!

Data Collection Tips

Good practices:

1. Ask the same question to everyone
2. Record data right away (don't trust memory!)
3. Be accurate and honest
4. Collect enough data (more is better)
5. Organize as you go

Common mistakes:

• Asking confusing questions
• Not recording all responses
• Making up data
• Collecting too little data
• Not organizing data clearly

Choosing the Right Graph

Use a bar graph when:

• Comparing different categories
• Data is in groups (favorite colors, types of pets)
• You want to show which is most/least

Use a line plot when:

• Data is on a number line
• You want to see the spread
• Looking for clusters or gaps
• Data involves measurements

Sample Data Project

Question: What is the most common shoe size in our class?

Step 1: Collect data

• Survey: "What is your shoe size?"
• Record each answer

Step 2: Organize with tally marks

Step 3: Create a line plot

        X
    X   X   X
    X   X   X
    X   X   X   X
   ─┼───┼───┼───┼──
    3   4   5   6
      Shoe Sizes

Step 4: Analyze

• Mode: Size 5 (appears 5 times)
• Range: 6 - 3 = 3
• Total students: 14

Step 5: Conclusion

• Most common shoe size is 5
• Sizes range from 3 to 6
• If ordering class shoes, order more size 5!

Advanced Analysis: Mean (Average)

Mean = Add all numbers, then divide by how many

Example scores: 7, 8, 9, 10, 6

Step 1: Add: 7+8+9+10+6 = 40 Step 2: Count how many: 5 scores Step 3: Divide: 40 ÷ 5 = 8

Mean = 8 ✓

What it tells us:

• The "typical" or average score
• Helpful for comparing groups

Common Mistakes When Analyzing

❌ Forgetting to count all data points ❌ Not organizing data first ❌ Choosing the wrong type of graph ❌ Drawing graphs without labels ❌ Making conclusions without looking at all the data

✅ Organize data in a table first ✅ Choose the right graph for your data ✅ Label everything clearly ✅ Look for patterns before concluding ✅ Make sure conclusions match the data

Data Analysis Checklist

When working with data, ask:

☐ Did I collect enough data? ☐ Is my data organized clearly? ☐ Did I choose the right type of graph? ☐ Does my graph have a title and labels? ☐ Did I look for patterns? ☐ Do my conclusions match what the data shows? ☐ Can I answer my original question?

Key Vocabulary

• Data: Information collected
• Survey: Asking questions to collect data
• Tally marks: Quick way to count
• Frequency: How often something appears
• Mode: Most common value
• Range: Difference between highest and lowest
• Cluster: Data grouped together
• Gap: Missing values in data
• Outlier: Value far from others
• Mean: The average

Why Data Matters

Data helps us:

• Make smart decisions
• Understand the world around us
• See what's working (or not working)
• Plan for the future
• Answer important questions

Real examples:

• Doctors collect data on patients to find best treatments
• Stores collect data on sales to know what to order
• Scientists collect data to make discoveries
• Your teacher collects data (test scores) to help you learn!

Summary

Creating data:

1. Ask a clear question
2. Collect information
3. Organize with tallies or tables

Analyzing data:

1. Make a graph
2. Look for patterns
3. Find mode, range, clusters
4. Draw conclusions
5. Answer your question

Remember: Good data starts with good questions, careful collection, and clear organization! ✓

The MOST common color: Blue (4 times) ✓

Blue is the mode (most frequent value)!