Overview
The Data & Measurement section of the TEAS exam tests your ability to calculate and interpret statistical measures, read and analyze various graph types, perform unit conversions between measurement systems, and solve basic probability problems. Mastery of these skills requires both conceptual understanding and careful arithmetic. This guide consolidates all key concepts, formulas, and strategies you need to succeed.
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Statistical Measures
Measures of Central Tendency
The three primary measures of central tendency describe the "center" of a data set.
• Mean – The arithmetic average. Add all values, then divide by the count of values.
- Formula: Mean = (Sum of all values) ÷ (Number of values)
- Example: {4, 8, 6, 10} → (4+8+6+10) ÷ 4 = 28 ÷ 4 = 7
• Median – The middle value when data is arranged in ascending order.
- Odd number of values: The middle value is the median.
- Even number of values: Average the two middle values.
- Example (odd): {2, 3, 5, 7, 9} → Median = 5
- Example (even): {2, 4, 6, 8} → Median = (6+8) ÷ 2 = 7
• Mode – The value that appears most frequently.
- A set can have no mode, one mode, or be bimodal (two equally frequent values).
- Example: {3, 3, 5, 7, 7} → Bimodal: 3 and 7
Measures of Spread
• Range – The difference between the largest and smallest values.
- Formula: Range = Maximum − Minimum
- Example: {2, 5, 9, 14} → 14 − 2 = 12
• Standard Deviation – Measures how spread out values are from the mean.
- High standard deviation = values are far from the mean (high variability)
- Low standard deviation = values are clustered near the mean (low variability)
The Impact of Outliers
| Measure | Effect of Outlier |
|---|---|
| Mean | Significantly affected — pulled toward the extreme value |
| Median | Minimally affected — remains stable |
| Mode | Not affected |
| Range | Significantly affected — increases dramatically |
> When a data set contains an outlier, the median is the more reliable measure of center.
Key Terms
• Outlier – A value significantly higher or lower than the rest of the data set
• Bimodal – A data set with exactly two values sharing the highest frequency
• Central tendency – A single value that describes the center of a data set
• Variability – How spread out the values in a data set are
⚠️ Watch Out For
• Always sort the data first before finding the median — a common mistake is using an unsorted list.
• Don't confuse mean and median: on exam questions with obvious outliers, the median is usually the "better" answer for typical value.
• A data set with all unique values has no mode — don't force one.
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Data Interpretation & Graphs
Types of Graphs and Their Uses
| Graph Type | Best Used For | Key Feature |
|---|---|---|
| Line Graph | Changes over time / trends | Connects data points with a line |
| Bar Graph | Comparing categories | Spaces between bars; categorical data |
| Histogram | Distribution of continuous data | No spaces between bars; uses intervals/ranges |
| Pie Chart | Parts of a whole | Sections must total 100% (or 360°) |
| Scatter Plot | Relationship between two variables | Shows correlation |
| Stem-and-Leaf Plot | Displaying individual values in groups | Preserves actual data values |
Reading Scatter Plots
• Positive correlation – Points trend upward left to right (both variables increase together)
• Negative correlation – Points trend downward left to right (one increases as the other decreases)
• No correlation – Points are scattered with no clear pattern
Calculating Percentages from Graphs
Use this formula when interpreting bar graphs or pie charts:
Percentage = (Part ÷ Whole) × 100
• Example: 40 out of 200 patients chose Option A → (40 ÷ 200) × 100 = 20%
Key Terms
• Histogram – Graph of continuous data using intervals with no gaps between bars
• Scatter plot – Graph showing the relationship (correlation) between two variables
• Stem-and-leaf plot – Display grouping data by leading digits while preserving all values
• Correlation – The relationship or association between two variables
⚠️ Watch Out For
• Bar graph vs. histogram: The key difference is the presence or absence of spaces between bars — this signals categorical vs. continuous data.
• Pie chart sections must always total 100% — use this to find a missing section by subtraction.
• When reading graphs, always check the scale and axis labels before interpreting data.
• A scatter plot shows correlation, not causation.
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Measurement Systems & Conversions
Metric System — Must-Know Conversions
| Conversion | Equivalent |
|---|---|
| 1 kilometer (km) | 1,000 meters (m) |
| 1 meter (m) | 100 centimeters (cm) |
| 1 centimeter (cm) | 10 millimeters (mm) |
| 1 kilogram (kg) | 1,000 grams (g) |
| 1 gram (g) | 1,000 milligrams (mg) |
| 1 liter (L) | 1,000 milliliters (mL) |
Memory tip for metric prefixes (largest to smallest):
> King Henry Died By Drinking Chocolate Milk
> (Kilo, Hecto, Deca, Base, Deci, Centi, Milli)
Metric-to-Imperial Conversions
| Conversion | Equivalent |
|---|---|
| 1 kg | ≈ 2.2 pounds (lbs) |
| 1 inch | = 2.54 cm |
| 1 mile | ≈ 1.6 km |
Conversion Strategy
• Multiplying to go smaller (e.g., m → cm): multiply by the conversion factor
- 2.5 m × 100 = 250 cm
• Dividing to go larger (e.g., lbs → kg): divide by the conversion factor
- 154 lbs ÷ 2.2 = 70 kg
⚠️ Watch Out For
• Multiply vs. divide: Moving to a smaller unit → multiply. Moving to a larger unit → divide.
• Be careful with multi-step conversions (e.g., km → cm requires two steps: km→m, then m→cm).
• Memorize the kg ↔ lbs conversion (2.2) as it appears frequently in nursing-context TEAS questions.
• Don't mix up milligrams and milliliters — similar prefixes, different types of measurement.
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Probability & Data Sets
Basic Probability Formula
P(event) = Number of favorable outcomes ÷ Total number of possible outcomes
• Probability always falls between 0 (impossible) and 1 (certain)
• Can be expressed as a fraction, decimal, or percentage
Examples
• Rolling a 3 on a six-sided die: P = 1/6
• Drawing a blue marble from a bag of 4 red, 3 blue, 5 green: P = 3/12 = 1/4
Independent vs. Dependent Events
| Event Type | Definition | Example |
|---|---|---|
| Independent | Outcome of one event does NOT affect the other | Flipping a coin twice |
| Dependent | Outcome of one event DOES affect the other | Drawing cards without replacement |
Choosing the Right Measure of Center
| Situation | Best Measure | Why |
|---|---|---|
| No outliers, symmetric data | Mean | Uses all values equally |
| Outliers present | Median | Not affected by extreme values |
| Categorical/repeated data | Mode | Identifies most common value |
> Example: {10, 20, 20, 30, 100}
> - Mean = 36 (skewed by outlier 100)
> - Median = 20 ✅ (better represents typical value)
Key Terms
• Probability – The likelihood of an event occurring, expressed as a ratio
• Favorable outcome – The specific result you are calculating probability for
• Independent events – Events where one outcome does not influence the other
• Dependent events – Events where one outcome changes the probability of the next
• Skewed – When a distribution is pulled in one direction by extreme values
⚠️ Watch Out For
• Always find the total number of outcomes first before writing a probability fraction.
• For dependent events (no replacement problems), remember the total decreases with each draw.
• Don't confuse "likely" with "certain" — a high probability still means the event might not occur.
• Re-read whether cards/marbles are drawn with or without replacement — this determines independence.
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Quick Review Checklist
Use this list to confirm you are ready for the TEAS Data & Measurement section:
• [ ] I can calculate the mean, median, mode, and range of any data set
• [ ] I know how to find the median for both odd and even numbered data sets
• [ ] I understand that outliers skew the mean but not the median
• [ ] I can identify when a data set is bimodal
• [ ] I know which graph type to use for different kinds of data
• [ ] I can distinguish a bar graph from a histogram
• [ ] I can calculate a percentage from a graph (Part ÷ Whole × 100)
• [ ] I know all essential metric conversions (mL/L, cm/m, g/kg, etc.)
• [ ] I can convert between pounds and kilograms using 2.2
• [ ] I know when to multiply vs. divide in unit conversions
• [ ] I can calculate basic probability using favorable ÷ total outcomes
• [ ] I understand the difference between independent and dependent events
• [ ] I can choose the best measure of central tendency for a given situation