Data, Statistics & Probability

Study Sheet

Data, Statistics & Probability

Measures of center and spread, reading and making graphs, interpreting data, and finding the probability of events as fractions, decimals, and percents

Mean, Median, and Mode (Measures of Center)

Concept
The Rule

A measure of center is one number that summarizes a whole list of numbers. There are three:

  • Mean (the average): add up all the values, then divide by how many values there are.
  • Median: the middle value once the numbers are put in order. If there are two middle values, take their mean (add them and divide by 22).
  • Mode: the value that appears most often. A data set can have one mode, several modes, or no mode at all.

Statistics summarizes data with a center (mean, median, mode) and a spread (range). Probability measures how likely an event is, as a fraction of equally likely outcomes: favorable over total. Listing the outcomes — a table for two dice, a tree for coin flips — is the reliable way to count them.

The three centers answer three different questions. The mean is the balance point: if the dots were equal weights on a ruler, the ruler would balance at the mean, because the distances below it exactly cancel the distances above it (33 and 33 sit 33 below 66; 77, 88, 99 sit 11, 22, 33 above, and 3+3=1+2+33 + 3 = 1 + 2 + 3). The median is the middle position, and the mode is the most popular value.

Example
Worked Example: all three centers
2345678910mean 6median 7mode 3
The data balance at the mean, 66; the middle dot is the median, 77.

Find the mean, median, and mode of 7, 3, 9, 3, 87,\ 3,\ 9,\ 3,\ 8.

Order first: 3, 3, 7, 8, 93,\ 3,\ 7,\ 8,\ 9.

Mean: 3+3+7+8+95=305=6\dfrac{3+3+7+8+9}{5} = \dfrac{30}{5} = \mathbf{6}.

Median: the middle of five ordered numbers is the 33rd one, so median =7= \mathbf{7}.

Mode: 33 appears twice, more than any other, so mode =3= \mathbf{3}.

Side note
Where it goes wrong

Taking the middle of the list as written: for 7,3,9,3,87, 3, 9, 3, 8, "the median is 99." The median is the middle of the ORDERED list, 3,3,7,8,93, 3, 7, 8, 9, which is 77.

Example
Worked Example: even number of values

Find the median of 10, 4, 6, 210,\ 4,\ 6,\ 2. Order them: 2, 4, 6, 102,\ 4,\ 6,\ 10. There are two middle values, 44 and 66, so the median is their mean: 4+62=5\dfrac{4+6}{2} = \mathbf{5}. Notice the median need not be a value in the list.

Concept
When Is Each One Most Useful?
  • Use the mean when the data are fairly even, with no extreme values.
  • Use the median when there is an outlier (a value far from the rest), because the median is not pulled toward it.
  • Use the mode for things you count or categories, like the most common shoe size or favorite color.
Example
Worked Example: effect of an outlier

Four friends have $55, $66, $77, and $88. Mean =264=$6.50= \dfrac{26}{4} = \$6.50; median =6+72=$6.50= \dfrac{6+7}{2} = \$6.50. Now a fifth friend joins with $100100 (an outlier). New mean =1265=$25.20= \dfrac{126}{5} = \$25.20, but the ordered list 5,6,7,8,1005,6,7,8,100 has median =$7= \mathbf{\$7}. The single big value pulled the mean way up, while the median barely moved. That is why the median better describes a “typical” friend here.

Tip

Tip: Always put the numbers in order before you find the median. Forgetting to order the data is the most common mistake in this whole topic.

Try it
Try it: a missing value

The mean of four test scores is 8585. Three of them are 8080, 9292 and 7878. What is the fourth score?

Show the answer

A mean of 8585 over 44 scores means the total is 4×85=3404 \times 85 = 340. The three known scores add to 80+92+78=25080 + 92 + 78 = 250, so the fourth is 340−250=90340 - 250 = 90.

Range (A Measure of Spread)

Concept
The Rule

The range tells how spread out the data are. It is the largest value minus the smallest value:

range=maximum−minimum.\text{range} = \text{maximum} - \text{minimum}.

A small range means the values are close together; a large range means they are spread far apart.

A center alone hides how consistent the data are: the scores 80,80,8080, 80, 80 and 60,80,10060, 80, 100 have the same mean and median, but the second set is far more spread out. The range measures that spread in the simplest way, as the width of the whole data set on the number line. Because it uses only the two extremes, one unusual value can change it a lot.

Example
Worked Example: finding the range
70747882869094range = 95 − 72 = 23
The range is the width of the data: from 7272 to 9595.

Test scores were 88, 95, 72, 90, 8188,\ 95,\ 72,\ 90,\ 81. The maximum is 9595 and the minimum is 7272, so the range =95−72=23= 95 - 72 = \mathbf{23}.

Side note
Where it goes wrong

Subtracting the first and last values as listed: 81−88=−781 - 88 = -7. The range uses the largest and smallest values, wherever they appear: 95−72=2395 - 72 = 23. A range is never negative.

Tip

Tip: Range is a single number, not an interval. Say “the range is 2323,” not “the range is 7272 to 9595.”

Try it
Try it: same center, different spread

Set A is 10,10,10,1010, 10, 10, 10 and set B is 4,8,12,164, 8, 12, 16. Compare their means and their ranges.

Show the answer

Both means are 1010: 404=10\tfrac{40}{4} = 10 each. Set A has range 10−10=010 - 10 = 0 (no spread at all); set B has range 16−4=1216 - 4 = 12. Same center, very different spread.

Reading and Making Graphs

Concept
The Rule

Different graphs are good for different jobs:

  • Bar graph: compares separate categories (favorite fruits, sports). Taller bar means larger amount.
  • Line graph: shows how something changes over time (temperature through the day). Look at whether the line rises, falls, or stays flat.
  • Circle (pie) graph: shows parts of one whole. All the slices together make 100%100\% of the total.
  • Dot plot / line plot: an ×\times or dot stacked above a number line for each data value; great for seeing repeats and the shape of the data.
  • Frequency table: lists each value or category next to how many times it occurs (its frequency).

Each kind of graph makes one comparison easy by turning it into something the eye judges well: bar heights for comparing categories, the slope of a line for change over time, slice sizes for parts of a whole, and stacked dots for how data are distributed. Picking a graph means asking which comparison the reader should see first.

Example
Worked Example: frequency table and dot plot
0123
One dot per student: the tallest stack is the mode.

A class recorded how many pets each student has:

pets0123students4632\begin{array}{c|cccc} \text{pets} & 0 & 1 & 2 & 3 \\ \hline \text{students} & 4 & 6 & 3 & 2 \end{array}

The frequency row tells us 4+6+3+2=154+6+3+2 = 15 students answered. The mode is 11 pet (highest frequency, 66). The dot plot stacks 44 dots above 00, 66 dots above 11, and so on. To find the total number of pets, multiply each value by its frequency and add: 0(4)+1(6)+2(3)+3(2)=0+6+6+6=180(4)+1(6)+2(3)+3(2) = 0+6+6+6 = 18 pets, so the mean is 1815=1.2\dfrac{18}{15} = 1.2 pets.

Side note
Where it goes wrong

Averaging the frequency row: "4+6+3+24=3.75\tfrac{4 + 6 + 3 + 2}{4} = 3.75 pets." Those numbers count students, not pets. The mean number of pets weights each value by how many students have it: 1815=1.2\tfrac{18}{15} = 1.2.

Example
Worked Example: reading a circle graph
Walk25%Bus50%Car25%
How 6060 students get to school.

A circle graph of how 6060 students get to school shows Walk =25%= 25\%, Bus =50%= 50\%, Car =25%= 25\%. Because the whole circle is 100%100\% of 6060 students, the bus slice stands for 50%50\% of 60=3060 = 30 students, and each 25%25\% slice stands for 1515 students. The slices 25%+50%+25%=100%25\% + 50\% + 25\% = 100\%, as they must.

Tip

Tip: On a circle graph the percents always add to 100%100\%. On any graph, first read the title and labels so you know what the numbers mean before you compare them.

Try it
Try it: read the circle graph

In a circle graph of 200200 votes, one slice is 35%35\%. How many votes is that slice, and what fraction of the circle does it fill?

Show the answer

The slice is 35%35\% of 200200: 0.35×200=700.35 \times 200 = 70 votes. As a fraction of the circle, 35%=35100=72035\% = \tfrac{35}{100} = \tfrac{7}{20}.

Interpreting Data from Tables and Graphs

Concept
The Rule

To interpret data, read carefully and answer the exact question asked. Good questions to ask yourself:

  • Which category is largest or smallest?
  • What is the total, or the difference between two values?
  • Is the data increasing, decreasing, or staying the same over time?

On a line graph the steepness of each segment is the rate of change for that interval: a steeper climb means more growth per week. That is why the biggest jump is visible before any subtraction, and why a flat segment always means "no change," whatever the height.

Example
Worked Example: interpreting a line graph
weekcm1234246810+4+1
The steepest segment (weeks 33 to 44) is the fastest growth.

A plant's height was measured each week:

week1234height (cm)25610\begin{array}{c|cccc} \text{week} & 1 & 2 & 3 & 4 \\ \hline \text{height (cm)} & 2 & 5 & 6 & 10 \end{array}

The plant grew every week, so the line rises the whole time. The weekly changes are 5−2=35 - 2 = 3, 6−5=16 - 5 = 1 and 10−6=410 - 6 = 4 cm, so the greatest growth was between Weeks 33 and 44. Total growth over the whole period was 10−2=810 - 2 = 8 cm.

Side note
Where it goes wrong

Reading the tallest point as the fastest growth. Week 44 is the highest point, but "fastest" asks about change, which is the steepest segment. A plant that is tall but barely growing has a high, flat line.

Tip

Tip: Watch the scale. If a bar graph counts by 55s, a bar reaching the third line means 1515, not 33.

Try it
Try it: average rate

Using the plant data, what was the average growth per week from Week 11 to Week 44?

Show the answer

Total growth is 10−2=810 - 2 = 8 cm over 4−1=34 - 1 = 3 weeks, so the average is 83≈2.7\tfrac{8}{3} \approx 2.7 cm per week. (Not 84=2\tfrac84 = 2: there are only three weekly steps between four measurements.)

Basic Probability

Concept
The Rule

Probability measures how likely an event is. An outcome is one possible result; a favorable outcome is a result you are hoping for. When every outcome is equally likely,

P(event)=number of favorable outcomestotal number of outcomes.P(\text{event}) = \frac{\text{number of favorable outcomes}}{\text{total number of outcomes}}.

The fraction works only because the outcomes are equally likely: each face of a fair cube has the same chance, so each carries 16\tfrac16 of the total certainty, and three favorable faces carry 36\tfrac36. When outcomes are not equally likely (a spinner with unequal sections, the sum of two dice), counting them is not enough; you must first break the experiment into outcomes that are.

Example
Worked Example: rolling a number cube
1234563 favorable (shaded) out of 6 equally likely
Even faces shaded: P(even)=36=12P(\text{even}) = \tfrac36 = \tfrac12.

A number cube has faces 1,2,3,4,5,61,2,3,4,5,6, so there are 66 equally likely outcomes. What is P(even)P(\text{even})? The favorable outcomes are 2,4,62,4,6, so 33 of them.

P(even)=36=12.P(\text{even}) = \frac{3}{6} = \frac{1}{2}.

What is P(a 5)P(\text{a }5)? Only one face works, so P=16P = \dfrac{1}{6}.

Side note
Where it goes wrong

Saying the sum of two dice is 77 with probability 111\tfrac{1}{11} because there are 1111 possible sums (22 to 1212). The sums are not equally likely: of the 3636 equally likely rolls, 66 give 77, so P=636=16P = \tfrac{6}{36} = \tfrac16.

Tip

Tip: Every probability is a number between 00 and 11. If your answer is negative or bigger than 11, you made a mistake. Count the total before you count the favorable outcomes.

Try it
Try it: a letter bag

The letters of the word PROBABILITY are put in a bag, one per tile. What is the probability of drawing a B? An I? A vowel?

Show the answer

There are 1111 tiles. B appears twice: P(B)=211P(\text{B}) = \tfrac{2}{11}. I appears twice: P(I)=211P(\text{I}) = \tfrac{2}{11}. The vowels are O, A, I, I: 44 tiles, so P(vowel)=411P(\text{vowel}) = \tfrac{4}{11} (Y is not counted as a vowel here).

Probability as a Fraction, Decimal, and Percent

Concept
The Rule

A probability can be written three equivalent ways. Start with the fraction, divide to get the decimal, then multiply by 100100 to get the percent. On the “likelihood” line:

  • P=0P = 0 means the event is impossible.
  • P=1P = 1 (that is, 100%100\%) means it is certain.
  • P=12P = \tfrac{1}{2} (50%50\%) means it is equally likely to happen or not. Larger than 12\tfrac12 is likely; smaller is unlikely.

Probability always lies between 00 and 11 because the favorable outcomes are some of the total outcomes: at best all of them (a fraction equal to 11), at worst none (a fraction equal to 00). The three forms are just three names for that same position on the scale from impossible to certain.

Example
Worked Example: three forms
01/41/23/41P(red)impossibleeven chancecertain
P(red)=14=0.25=25%P(\text{red}) = \tfrac14 = 0.25 = 25\%, closer to impossible than to certain.

A bag has 2020 marbles, and 55 are red. Then

P(red)=520=14=0.25=25%.P(\text{red}) = \frac{5}{20} = \frac{1}{4} = 0.25 = 25\%.

Since 25%25\% is less than 50%50\%, drawing red is unlikely. Drawing “a marble” at all is certain: P=2020=1=100%P = \dfrac{20}{20} = 1 = 100\%. Drawing a green marble is impossible (there are none): P=0P = 0.

Side note
Where it goes wrong

Writing a probability of 25%25\% as 2525 or as 0.0250.025. 25%=25100=0.2525\% = \tfrac{25}{100} = 0.25; move the decimal point two places, not one or three.

Tip

Tip: To turn a fraction into a percent, divide top by bottom, then move the decimal point two places right. 34=0.75=75%\dfrac{3}{4} = 0.75 = 75\%.

Try it
Try it: three forms

A spinner has 88 equal sections, 33 of them blue. Write P(blue)P(\text{blue}) as a fraction, a decimal and a percent. Is blue likely or unlikely?

Show the answer

P(blue)=38P(\text{blue}) = \tfrac38. As a decimal, 3÷8=0.3753 \div 8 = 0.375; as a percent, 37.5%37.5\%. That is less than 50%50\%, so blue is unlikely.

Compound Events and the Counting Principle

Concept
The Rule

A compound event involves more than one thing happening (such as flipping a coin and rolling a cube). The Counting Principle says: if one choice can happen in mm ways and a second choice in nn ways, then together they can happen in

m×n ways.m \times n \text{ ways}.

This gives the total number of outcomes, which you can then use in a probability fraction.

The tree shows why the numbers multiply: each of the 22 coin results starts its own full set of 66 cube results, so the total is 6+6=2×66 + 6 = 2 \times 6 branches. With more stages the branching repeats, which is why three stages with mm, nn and pp choices give m×n×pm \times n \times p outcomes.

Example
Worked Example: counting outfits

You have 33 shirts and 44 pairs of shorts. How many different outfits? By the Counting Principle, 3×4=123 \times 4 = \mathbf{12} outfits.

Side note
Where it goes wrong

Counting outcomes that are not equally likely as if they were. Two coins have four outcomes (HH, HT, TH, TT), not three; "one head" happens two ways, so P(exactly one head)=24=12P(\text{exactly one head}) = \tfrac24 = \tfrac12, not 13\tfrac13.

Example
Worked Example: coin and cube together
H123456T123456
Every coin result branches into all six cube results: 2×6=122 \times 6 = 12. One leaf is H 3.

Flip a coin (22 outcomes: H, T) and roll a cube (66 outcomes). Total outcomes =2×6=12= 2 \times 6 = 12. What is P(heads and a 3)P(\text{heads and a }3)? Only one pair works out of 1212, so

P(H and 3)=112.P(\text{H and }3) = \frac{1}{12}.
Tip

Tip: For “and” with the Counting Principle you multiply the numbers of ways. A tree diagram is a great way to list every outcome when the numbers are small.

Try it
Try it: a lunch menu

A lunch special offers 33 sandwiches, 22 sides and 44 drinks. How many different lunches are there? If one is chosen at random, what is the probability it includes the one soup side?

Show the answer

By the Counting Principle there are 3×2×4=243 \times 2 \times 4 = 24 lunches. Exactly half of them use the soup side (for each sandwich and drink, the side is soup or the other one), so the probability is 1224=12\tfrac{12}{24} = \tfrac12.

Real-World Data and Probability Problems

Concept
The Rule

Word problems combine these ideas. Read slowly and decide what is being asked: a center (mean, median, mode), a spread (range), a reading from a graph, or a probability. Then set up the matching calculation and check that your answer makes sense.

Real questions rarely name the tool. "What was typical?" asks for a center, "how consistent?" for a spread, and "how likely?" for a probability. Deciding which question is being asked comes before any computing, and a sanity check comes after: a mean must sit between the smallest and largest values, and a probability between 00 and 11.

Example
Worked Example: putting it together

In five games a player scored 12, 8, 15, 12, 312,\ 8,\ 15,\ 12,\ 3 points.

Mean: 12+8+15+12+35=505=10\dfrac{12+8+15+12+3}{5} = \dfrac{50}{5} = 10 points.

Median (order 3,8,12,12,153,8,12,12,15): the middle value is 1212.

Mode: 1212 (it appears twice).   Range: 15−3=1215 - 3 = 12.

If the coach picks one of these five games at random to rewatch, the probability it is a game where the player scored more than 1010 points (the 1212, 1515, and 1212 games =3=3 games) is 35=0.6=60%\dfrac{3}{5} = 0.6 = 60\% — likely.

Side note
Where it goes wrong

Reporting the mean as "typical" when one value is far from the rest. For 3,8,12,12,153, 8, 12, 12, 15 the mean 1010 and median 1212 differ because the 33 drags the mean down; the median describes a typical game better here.

Tip

Tip: After solving, reread the question. Answers should have the right units (points, students, %) and land in a sensible range — probabilities between 00 and 11, and a mean somewhere between the smallest and largest data values.

Try it
Try it: scores

In six games a team scored 4,7,7,9,10,234, 7, 7, 9, 10, 23. Find the mean, median, mode and range. Which center best describes a typical game?

Show the answer

Mean: 4+7+7+9+10+236=606=10\tfrac{4 + 7 + 7 + 9 + 10 + 23}{6} = \tfrac{60}{6} = 10. Median: the middle two are 77 and 99, so 7+92=8\tfrac{7 + 9}{2} = 8. Mode: 77. Range: 23−4=1923 - 4 = 19. The 2323 is far from the rest and pulls the mean up to 1010, above four of the six scores, so the median 88 is the better description of a typical game.

Going Deeper: Advanced Statistics & Probability

Concept
The Weighted Mean

A plain mean treats every value equally. A weighted mean lets some values count more than others. If each value xx has a weight ww (how many times it counts), then

weighted mean=∑(x⋅w)∑w=x1w1+x2w2+⋯w1+w2+⋯.\text{weighted mean} = \frac{\sum (x \cdot w)}{\sum w} = \frac{x_1 w_1 + x_2 w_2 + \cdots}{w_1 + w_2 + \cdots}.

This is exactly how a course grade works when tests count more than homework.

Example
Worked Example: a weighted class grade

Homework is worth 20%20\%, quizzes 30%30\%, and the final exam 50%50\%. A student earns 9090, 8080, and 7070 on these. Multiply each score by its weight (as a decimal) and add:

grade=90(0.20)+80(0.30)+70(0.50)=18+24+35=77.\text{grade} = 90(0.20) + 80(0.30) + 70(0.50) = 18 + 24 + 35 = \mathbf{77}.

The plain average would be 90+80+703=80\dfrac{90+80+70}{3} = 80, but because the heavily weighted final was lowest, the weighted grade drops to 7777. The weights 0.20+0.30+0.50=10.20+0.30+0.50 = 1 must add to 11 (that is 100%100\%).

Concept
Measuring Spread: MAD and Standard Deviation

Range uses only two values. Two better measures of spread use every value's distance from the mean.

  • Mean Absolute Deviation (MAD): average how far each value is from the mean, using positive distances. @@BLOCK0@@
  • Standard deviation (σ\sigma): instead of absolute values, square each distance, average the squares (this average is the variance), then take the square root. @@BLOCK1@@

For both, a larger value means the data are more spread out. Standard deviation is the one you will meet most in later courses.

Example
Worked Example: MAD and standard deviation
024681012mean 64224
Each arrow is one distance from the mean; MAD averages them.

Find the MAD and standard deviation of 2, 4, 6, 8, 102,\ 4,\ 6,\ 8,\ 10. First the mean: xˉ=2+4+6+8+105=305=6\bar{x} = \dfrac{2+4+6+8+10}{5} = \dfrac{30}{5} = 6. Now each distance from 66:

x246810∣x−6∣42024(x−6)21640416\begin{array}{c|ccccc} x & 2 & 4 & 6 & 8 & 10 \\ \hline |x - 6| & 4 & 2 & 0 & 2 & 4 \\ (x - 6)^2 & 16 & 4 & 0 & 4 & 16 \end{array}

MAD: 4+2+0+2+45=125=2.4\dfrac{4+2+0+2+4}{5} = \dfrac{12}{5} = \mathbf{2.4}.

Standard deviation: the squared distances sum to 16+4+0+4+16=4016+4+0+4+16 = 40, so the variance is 405=8\dfrac{40}{5} = 8 and σ=8≈2.83\sigma = \sqrt{8} \approx \mathbf{2.83}. Both numbers describe the “typical” distance of a value from the mean.

Concept
Quartiles, IQR, and Outliers

The median splits ordered data in half. The quartiles split it into quarters:

  • Q1Q_1 (lower quartile) is the median of the lower half.
  • Q2Q_2 is the overall median.
  • Q3Q_3 (upper quartile) is the median of the upper half.

The interquartile range is IQR=Q3−Q1\text{IQR} = Q_3 - Q_1; it measures the spread of the middle 50%50\% of the data and ignores extremes. A value is usually called an outlier if it lies below Q1−1.5(IQR)Q_1 - 1.5(\text{IQR}) or above Q3+1.5(IQR)Q_3 + 1.5(\text{IQR}).

Example
Worked Example: quartiles, IQR, and an outlier check
0246810121416182022fence 19.520Q₁ 7median 9Q₃ 12
Box from Q1Q_1 to Q3Q_3; the 2020 lies past the upper fence.

For 4, 7, 8, 9, 11, 12, 204,\ 7,\ 8,\ 9,\ 11,\ 12,\ 20 (already in order, n=7n=7), the median is the 44th value, Q2=9Q_2 = 9. The lower half is 4,7,84,7,8, so Q1=7Q_1 = 7; the upper half is 11,12,2011,12,20, so Q3=12Q_3 = 12. Then

IQR=Q3−Q1=12−7=5.\text{IQR} = Q_3 - Q_1 = 12 - 7 = 5.

Outlier fences: Q1−1.5(5)=7−7.5=−0.5Q_1 - 1.5(5) = 7 - 7.5 = -0.5 and Q3+1.5(5)=12+7.5=19.5Q_3 + 1.5(5) = 12 + 7.5 = 19.5. Since 20>19.520 > 19.5, the value 20\mathbf{20} is an outlier.

Concept
Permutations and Combinations

When the Counting Principle is used to arrange or choose from one group, two special cases appear. Write n!=n(n−1)(n−2)⋯1n! = n(n-1)(n-2)\cdots 1 (read “nn factorial”).

  • A permutation counts arrangements where order matters (like finishing 11st, 22nd, 33rd): @@BLOCK0@@
  • A combination counts selections where order does not matter (like choosing a team): @@BLOCK1@@

Because order matters in more cases, nPr{}_nP_r is always at least as large as nCr{}_nC_r.

Example
Worked Example: order matters vs. order does not

From 55 runners, how many ways can they take gold, silver, and bronze? Order matters, so

5P3=5!(5−3)!=5!2!=1202=60.{}_5P_3 = \frac{5!}{(5-3)!} = \frac{5!}{2!} = \frac{120}{2} = \mathbf{60}.

From those same 55 runners, how many ways can we pick 33 to form a relay team (no ordering)? Now

5C3=5!3! 2!=1206⋅2=12012=10.{}_5C_3 = \frac{5!}{3!\,2!} = \frac{120}{6 \cdot 2} = \frac{120}{12} = \mathbf{10}.

Each unordered team of 33 can be ordered in 3!=63! = 6 ways, and indeed 10×6=6010 \times 6 = 60, matching the permutation count.

Concept
Expected Value

The expected value is the long-run average outcome of a random situation. Multiply each outcome's value by its probability, then add:

E=∑(value×probability).E = \sum (\text{value} \times \text{probability}).

It need not be a possible single outcome; it is what you would average over many, many repeats.

Example
Worked Example: is the game fair?

A game costs $22 to play. You roll a number cube: rolling a 66 wins you $1010; anything else wins nothing. The expected winnings are

E=10⋅16+0⋅56=106≈$1.67.E = 10 \cdot \frac{1}{6} + 0 \cdot \frac{5}{6} = \frac{10}{6} \approx \$1.67.

Since the expected win $1.671.67 is less than the $22 cost, the game favors the house: on average you lose about $0.330.33 each play.

Concept
Independent vs. Dependent Events and Conditional Probability

Two events are independent if one happening does not change the other's probability (like two separate coin flips). They are dependent if it does (like drawing cards without replacing them).

  • Independent “and”: P(A and B)=P(A)⋅P(B)P(A \text{ and } B) = P(A) \cdot P(B).
  • Dependent “and”: P(A and B)=P(A)⋅P(B given A)P(A \text{ and } B) = P(A) \cdot P(B \text{ given } A), where P(B given A)P(B \text{ given } A) is the conditional probability of BB once AA has happened.
Example
Worked Example: drawing without replacement

A bag has 33 red and 22 blue marbles (55 total). You draw two marbles without putting the first back. Find P(both red)P(\text{both red}). The first draw is red with probability 35\dfrac{3}{5}. Now only 22 red remain out of 44 marbles, so the second is red with probability 24\dfrac{2}{4}. These are dependent, so multiply:

P(both red)=35⋅24=620=310.P(\text{both red}) = \frac{3}{5} \cdot \frac{2}{4} = \frac{6}{20} = \frac{3}{10}.

If instead you replaced the first marble, the draws would be independent and P=35⋅35=925P = \dfrac{3}{5} \cdot \dfrac{3}{5} = \dfrac{9}{25}.

Tip

Tip: The word “and” tells you to multiply probabilities; the word “or” (for events that cannot both happen) tells you to add them. Always ask whether the second event's probability changed — if it did, the events are dependent.

Concept
“At Least One” via the Complement

The complement of an event is everything except that event, and P(not A)=1−P(A)P(\text{not }A) = 1 - P(A). Problems that ask for the probability of “at least one” success are usually far easier through the complement, because “at least one” is the opposite of “none”:

P(at least one)=1−P(none).P(\text{at least one}) = 1 - P(\text{none}).
Example
Worked Example: at least one head
HHHHHHTHHTTHHTHTHTTTHHTHHTTHTTHTTHTTTT
Of the 88 equally likely paths, only TTT has no head.

Flip a fair coin 33 times. Find P(at least one head)P(\text{at least one head}). Listing all the winning cases is tedious, so use the complement. The only way to get no heads is all tails: P(TTT)=12⋅12⋅12=18P(\text{TTT}) = \dfrac{1}{2} \cdot \dfrac{1}{2} \cdot \dfrac{1}{2} = \dfrac{1}{8}. Therefore

P(at least one head)=1−18=78.P(\text{at least one head}) = 1 - \frac{1}{8} = \frac{7}{8}.
Tip

Tip: Whenever a probability question contains the phrase “at least one,” try the complement first: find P(none)P(\text{none}) and subtract from 11. It almost always saves work.

Why It Works

Probability as favorable/total only works when every outcome is equally likely, which is why organized listing matters: it makes the outcomes genuinely equal. The mean balances the data (deviations sum to zero), so an outlier shifts it; the median only counts positions, so an outlier cannot move it more than one place. Choosing the right summary depends on which kind of question you are answering.

Tip
Reading a proof

Do not read a proof the way you read a rule. Cover the last line, ask what the previous line forces, and only then uncover it. If a step feels like magic, that is the step to work by hand with a concrete number.

Formulas, Proofs & Tips

Tip
Mean, median, mode, range
xˉ=x1+x2+⋯+xnn\bar{x}=\frac{x_1+x_2+\cdots+x_n}{n}

What it means. The mean is the balancing point; the median is the middle value once sorted; the mode is the most frequent value; the range is largest minus smallest.

Example. For 3,5,5,93,5,5,9: mean =224=5.5=\tfrac{22}{4}=5.5, median =5=5, mode =5=5, range =9−3=6=9-3=6.

Why it works. The mean shares the total equally among the nn values: if everyone had xˉ\bar{x}, the total would still be nxˉ=∑xin\bar{x}=\sum x_i.

Tip. Sort the list before taking a median. With an even count the median is the average of the two middle values. One extreme outlier drags the mean but barely moves the median.

Tip
Standard deviation
σ=∑(xi−xˉ)2n\sigma=\sqrt{\frac{\sum (x_i-\bar{x})^{2}}{n}}

What it means. The typical distance of a value from the mean.

Example. For 2,4,62,4,6: mean 44, so σ=4+0+43=83≈1.63\sigma=\sqrt{\tfrac{4+0+4}{3}}=\sqrt{\tfrac83}\approx1.63.

Why it works. Raw deviations xi−xˉx_i-\bar x sum to zero, so they are squared to stop cancellation, averaged to get a typical squared distance, then square-rooted to return to the original units.

Tip. Adding a constant to every value leaves σ\sigma unchanged; multiplying every value by kk multiplies σ\sigma by ∣k∣|k|.