experimental probability graph

Experimental Probability

The chance or occurrence of a particular event is termed its probability. The value of a probability lies between 0 and 1 which means if it is an impossible event, the probability is 0 and if it is a certain event, the probability is 1. The probability that is determined on the basis of the results of an experiment is known as experimental probability. This is also known as empirical probability.

What is Experimental Probability?

Experimental probability is a probability that is determined on the basis of a series of experiments. A random experiment is done and is repeated many times to determine their likelihood and each repetition is known as a trial. The experiment is conducted to find the chance of an event to occur or not to occur. It can be tossing a coin, rolling a die, or rotating a spinner. In mathematical terms, the probability of an event is equal to the number of times an event occurred ÷ the total number of trials. For instance, you flip a coin 30 times and record whether you get a head or a tail. The experimental probability of obtaining a head is calculated as a fraction of the number of recorded heads and the total number of tosses. P(head) = Number of heads recorded ÷ 30 tosses.

Experimental Probability Formula

The experimental probability of an event is based on the number of times the event has occurred during the experiment and the total number of times the experiment was conducted. Each possible outcome is uncertain and the set of all the possible outcomes is called the sample space. The formula to calculate the experimental probability is: P(E) = Number of times an event occurs/Total number of times the experiment is conducted

Consider an experiment of rotating a spinner 50 times. The table given below shows the results of the experiment conducted. Let us find the experimental probability of spinning the color - blue.

The experimental probability of spinning the color blue = 10/50 = 1/5 = 0.2 = 20%

Experimental Probability vs Theoretical Probability

Experimental results are unpredictable and may not necessarily match the theoretical results. The results of experimental probability are close to theoretical only if the number of trials is more in number. Let us see the difference between experimental probability and theoretical probability.

Experimental Probability Examples

Here are a few examples from real-life scenarios.

a) The number of cookies made by Patrick per day in this week is given as 4, 7, 6, 9, 5, 9, 5.

Based on this data, what is the reasonable estimate of the probability that Patrick makes less than 6 cookies the next day?

P(< 6 cookies) = 3/7 = 0.428 = 42%

b) Find the reasonable estimate of the probability that while ordering a pizza, the next order will not be of a pepperoni topping.

Based on this data , the reasonable estimate of the probability that the next type of toppings that would get ordered is not a pepperoni will be 15/20 = 3/4 = 75%

Related Sections

• Card Probability
• Conditional Probability Calculator
• Binomial Probability Calculator
• Probability Rules
• Probability and Statistics

Important Notes

• The sum of the experimental probabilities of all the outcomes is 1.
• The probability of an event lies between 0 and 1, where 0 is an impossible event and 1 denotes a certain event.
• Probability can also be expressed in percentage.

Examples on Experimental Probability

Example 1: The following table shows the recording of the outcomes on throwing a 6-sided die 100 times.

Find the experimental probability of: a) Rolling a four; b) Rolling a number less than four; c) Rolling a 2 or 5

Experimental probability is calculated by the formula: Number of times an event occurs/Total number of trials

a) Rolling a 4: 17/100 = 0.17

b) Rolling a number less than 4: 56/100 = 0.56

c) Rolling a 2 or 5: 31/100 = 0.31

Example 2: The following set of data shows the number of messages that Mike received recently from 6 of his friends. 4, 3, 2, 1, 6, 8. Based on this, find the probability that Mike will receive less than 2 messages next time.

Mike has received less than 2 messages from 2 of his friends out of 6.

Therefore, P(<2) = 2/6 = 1/3

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Practice Questions on Experimental Probability

Frequently asked questions (faqs), how do you find the experimental probability.

The experimental probability of an event is based on actual experiments and the recordings of the events. It is equal to the number of times an event occurred divided by the total number of trials.

What is the Experimental Probability of rolling a 6?

The experimental probability of rolling a 6 is 1/6. A die has 6 faces numbered from 1 to 6. Rolling the die to get any number from 1 to 6 is the same and the probability (of getting a 6) = Number of favorable outcomes/ total possible outcomes = 1/6.

What is the Difference Between Theoretical and Experimental Probability?

Theoretical probability is what is expected to happen and experimental probability is what has actually happened in the experiment.

Do You Simplify Experimental Probability?

Yes, after finding the ratio of the number of times the event occurred to the total number of trials conducted, the fraction which is obtained is simplified.

Which Probability is More Accurate, Theoretical Probability or Experimental Probability?

Theoretical probability is more accurate than experimental probability. The results of experimental probability are close to theoretical only if the number of trials are more in number.

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Experimental Probability

Here we will learn about experimental probability, including using the relative frequency and finding the probability distribution.

There are also probability distribution worksheets based on Edexcel, AQA and OCR exam questions, along with further guidance on where to go next if you’re still stuck.

What is experimental probability?

Experimental probability i s the probability of an event happening based on an experiment or observation.

To calculate the experimental probability of an event, we calculate the relative frequency of the event.

We can also express this as R=\frac{f}{n} where R is the relative frequency, f is the frequency of the event occurring, and n is the number of trials of the experiment.

If we find the relative frequency for all possible events from the experiment we can write the probability distribution for that experiment.

The relative frequency, experimental probability and empirical probability are the same thing and are calculated using the data from random experiments. They also have a key use in real-life problem solving.

For example, Jo made a four-sided spinner out of cardboard and a pencil.

She spun the spinner 50 times. The table shows the number of times the spinner landed on each of the numbers 1 to 4. The final column shows the relative frequency.

The relative frequencies of all possible events will add up to 1.

This is because the events are mutually exclusive.

Step-by-step guide: Mutually exclusive events

Experimental probability vs theoretical probability

You can see that the relative frequencies are not equal to the theoretical probabilities we would expect if the spinner was fair.

If the spinner is fair, the more times an experiment is done the closer the relative frequencies should be to the theoretical probabilities.

In this case the theoretical probability of each section of the spinner would be 0.25, or \frac{1}{4}.

Step-by-step guide: Theoretical probability

How to find an experimental probability distribution

In order to calculate an experimental probability distribution:

Draw a table showing the frequency of each outcome in the experiment.

Determine the total number of trials.

Write the experimental probability (relative frequency) of the required outcome(s).

Explain how to find an experimental probability distribution

Experimental probability worksheet

Get your free experimental probability worksheet of 20+ questions and answers. Includes reasoning and applied questions.

Related lessons on   probability distribution

Experimental probability  is part of our series of lessons to support revision on  probability distribution . You may find it helpful to start with the main probability distribution lesson for a summary of what to expect, or use the step by step guides below for further detail on individual topics. Other lessons in this series include:

• Probability distribution
• Relative frequency
• Expected frequency

Experimental probability examples

Example 1: finding an experimental probability distribution.

A 3 sided spinner numbered 1,2, and 3 is spun and the results recorded.

Find the probability distribution for the 3 sided spinner from these experimental results.

A table of results has already been provided. We can add an extra column for the relative frequencies.

2 Determine the total number of trials

3 Write the experimental probability (relative frequency) of the required outcome(s).

Divide each frequency by 110 to find the relative frequencies.

Example 2: finding an experimental probability distribution

A normal 6 sided die is rolled 50 times. A tally chart was used to record the results.

Determine the probability distribution for the 6 sided die. Give your answers as decimals.

Use the tally chart to find the frequencies and add a row for the relative frequencies.

The question stated that the experiment had 50 trials. We can also check that the frequencies add to 50.

Divide each frequency by 50 to find the relative frequencies.

Example 3: using an experimental probability distribution

A student made a biased die and wanted to find its probability distribution for use in a game. They rolled the die 100 times and recorded the results.

By calculating the probability distribution for the die, determine the probability of the die landing on a 3 or a 4.

The die was rolled 100 times.

We can find the probability of rolling a 3 or a 4 by adding the relative frequencies for those numbers.

P(3 or 4) = 0.22 + 0.25 = 0.47

Example 4: calculating the relative frequency without a known frequency of outcomes

A research study asked 1200 people how they commute to work. 640 travelled by car, 174 used the bus, and the rest walked. Determine the relative frequency of someone not commuting to work by car.

Writing the known information into a table, we have

We currently do not know the frequency of people who walked to work. We can calculate this as we know the total frequency.

The number of people who walked to work is equal to

1200-(640+174)=386.

We now have the full table,

The total frequency is 1200.

Divide each frequency by the total number of people (1200), we have

The relative frequency of someone walking to work is 0.321\dot{6} .

How to find a frequency using an experimental probability

In order to calculate a frequency using an experimental probability:

Multiply the total frequency by the experimental probability.

Explain how to find a frequency using an experimental probability

Example 5: calculating a frequency

A dice was rolled 300 times. The experimental probability of rolling an even number is \frac{27}{50}. How many times was an even number rolled?

An even number was rolled 162 times.

Example 6: calculating a frequency

A bag contains different coloured counters. A counter is selected at random and replaced back into the bag 240 times. The probability distribution of the experiment is given below.

Determine the number of times a blue counter was selected.

As the events are mutually exclusive, the sum of the probabilities must be equal to 1. This means that we can determine the value of x.

1-(0.4+0.25+0.15)=0.2

The experimental probability (relative frequency) of a blue counter is 0.2.

Multiplying the total frequency by 0.1, we have

240 \times 0.2=48.

A blue counter was selected 48 times.

Common misconceptions

• Forgetting the differences between theoretical and experimental probability

It is common to forget to use the relative frequencies from experiments for probability questions and use the theoretical probabilities instead. For example, they may be asked to find the probability of a die landing on an even number based on an experiment and the student will incorrectly answer it as 0.5.

• The relative frequency is not an integer

The relative frequency is the same as the experimental probability. This value is written as a fraction, decimal or percentage, not an integer.

Practice experimental probability questions

1. A coin is flipped 80 times and the results recorded.

Determine the probability distribution of the coin.

As the number of tosses is 80, dividing the frequencies for the number of heads and the number of tails by 80, we have

2. A 6 sided die is rolled 160 times and the results recorded.

Determine the probability distribution of the die. Write your answers as fractions in their simplest form.

Dividing the frequencies of each number by 160, we get

3. A 3 -sided spinner is spun and the results recorded.

Find the probability distribution of the spinner, giving you answers as decimals to 2 decimal places.

Dividing the frequencies of each colour by 128 and simplifying, we have

4. A 3 -sided spinner is spun and the results recorded.

Find the probability of the spinner not landing on red. Give your answer as a fraction.

Add the frequencies of blue and green and divide by 128.

5. A card is picked at random from a deck and then replaced. This was repeated 4000 times. The probability distribution of the experiment is given below.

How many times was a club picked?

6. Find the missing frequency from the probability distribution.

The total frequency is calculated by dividing the frequency by the relative frequency.

Experimental probability GCSE questions

1. A 4 sided spinner was spun in an experiment and the results recorded.

(a) Complete the relative frequency column. Give your answers as decimals.

(b) Find the probability of the spinner landing on a square number.

Total frequency of 80.

2 relative frequencies correct.

All 4 relative frequencies correct 0.225, \ 0.2, \ 0.3375, \ 0.2375.

Relative frequencies of 1 and 4 used.

0.4625 or equivalent

2. A 3 sided spinner was spun and the results recorded.

Complete the table.

Process to find total frequency or use of ratio with 36 and 0.3.

3. Ben flipped a coin 20 times and recorded the results.

(a) Ben says, “the coin must be biased because I got a lot more heads than tails”.

Comment on Ben’s statement.

(b) Fred takes the same coin and flips it another 80 times and records the results.

Use the information to find a probability distribution for the coin.

Stating that Ben’s statement may be false.

Mentioning that 20 times is not enough trials.

Evidence of use of both sets of results from Ben and Fred.

Process of dividing by 100.

P(heads) = 0.48 or equivalent

P(tails) = 0.52 or equivalent

Learning checklist

You have now learned how to:

• Use a probability model to predict the outcomes of future experiments; understand that empirical unbiased samples tend towards theoretical probability distributions, with increasing sample size

The next lessons are

• How to calculate probability
• Combined events probability
• Describing probability

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Experimental Probability

At first we will know the precise meaning of the term ‘experiment’ and the proper context in which it will be used in our experimental probability.

Definition of experiment:  A process which can produce some well-defined results (outcomes) is called an experiment.

Some Experiments and their outcomes:

I. Tossing a coin:  Suppose we toss a coin and let it fall flat on the ground. Its upper face will show either Head (H) or Tail (T).

            1. Whatever comes up, is called an outcome.

            2. All possible outcomes are Head (H) and Tail (T).

II. Throwing a dice: 

A dice is a solid cube having 6 faces, marked as 1, 2, 3, 4, 5, 6 respectively. Suppose we throw a dice and let it fall flat on the ground. Its upper face will show one of the numbers 1, 2, 3, 4, 5, 6.

• Whatever comes up, is called an outcome.
• All possible outcomes are 1, 2, 3, 4, 5, 6.

The act of tossing a coin or throwing a dice is called an experiment. Whatever comes up, is called an outcome. In an experiment, all possible outcomes are known.

The plural of die is dice.

III. Drawing a card from a well-shuffled deck of 52 cards:

A deck of playing cards has in all 52 cards.

1. It has 13 cards each of four suits, namely spades , clubs , hearts and diamonds .

• Cards of spades and clubs are black cards.
• Cards of hearts and diamonds are red cards.

2. Kings, queens and jacks (or knaves) are known as face cards. Thus, there are 12 face cards in all.

Definition of Experimental Probability: The experimental probability of happening of an event is the ratio of the number of trials in which the event happened to the total number of trials.

The experimental probability of the occurrence of an event E is defined as:

Solved examples on Experimental Probability:

1. Suppose we toss a coin 100 times and get a head 58 times. Now, we toss a coin at random. What is the probability of getting a head?

Total number of trials = 100. Number of times head appeared = 58.

2. A coin is tossed 150 times and head is obtained 71 times. Now, if a coin is tossed at random, what is the probability of getting a tail?

Total number of trials = 150. Number of times head appeared = 71. Number of times tail appeared = (150 - 71) = 79.

• Probability

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Events in Probability

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Coin Toss Probability

Probability of Tossing Two Coins

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Conditional Probability

Theoretical Probability

Odds and Probability

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Probability and Playing Cards

Probability for Rolling Two Dice

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Experimental Probability

Experimental probability: introduction, experimental probability: definition, experimental probability formula, solved examples, practice problems, frequently asked questions.

In mathematics, probability refers to the chance of occurrence of a specific event. Probability can be measured on a scale from 0 to 1. The probability is 0 for an impossible event. The probability is 1 if the occurrence of the event is certain.

There are two approaches to study probability: experimental and theoretical. 

Suppose you and your friend toss a coin to decide who gets the first turn to ride a new bicycle. You choose “heads” and your friend chooses “tails.” 

Can you guess who will win? No! You have $\frac{1}{2}$ a chance of winning and so does your friend. This is theoretical since you are predicting the outcome based on what is expected to happen and not on the basis of outcomes of an experiment.

So, what is the experimental probability? Experimental probability is calculated by repeating an experiment and observing the outcomes. Let’s understand this a little better.

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Experimental probability, or empirical probability, is the probability calculated by performing actual experiments and gathering or recording the necessary information. How would you define an experiment? The math definition of an experiment is “a process or procedure that can be repeated and that has a set of well-defined possible results or outcomes.”

Consider the same example. Suppose you flip the coin 50 times to see whether you get heads or tails, and you record the outcomes. Suppose you get heads 20 times and tails 30 times. Then the probability calculated using these outcomes is experimental probability. Here, t he experimental meaning is connected with such experiments used to determine the probability of an event.

Now that you know the meaning of experimental probability, let’s understand its formula.

Experimental Probability for an Event A can be calculated as follows:

P(E) $= \frac{Number of occurance of the event A}{Total number of trials}$

Let’s understand this with the help of the last example. 

A coin is flipped a total of 50 times. Heads appeared 20 times. Now, what is the experimental probability of getting heads?

E xperimental probability of getting heads $= \frac{Number of occurrences}{Total number of trials}$

P (Heads) $= \frac{20}{50} = \frac{2}{5}$

P (Tails) $= \frac{30}{50} = \frac{3}{5}$

Experimental Probability vs. Theoretical Probability

Theoretical probability expresses what is expected. On the other hand, experimental probability explains how frequently an event occurred in an experiment.

If you roll a die, the theoretical probability of getting any particular number, say 3, is $\frac{1}{6}$. 

However, if you roll the die 100 times and record how many times 3 appears on top, say 65 times, then the experimental probability of getting 3 is $\frac{65}{100}$.

Theoretical probability for Event A can be calculated as follows:

P(A) $= \frac{Number of outcomes favorable to Event A}{Number of possible outcomes}$

In the example of flipping a coin, the theoretical probability of the occurrence of heads (or tails) on tossing a coin is

P(H) $= \frac{1}{2}$ and  P(T) $= \frac{1}{2}$ (since possible outcomes are $2 -$ head or tail)

Experimental Probability: Examples

Let’s take a look at some of the examples of experimental probability .

Example 1: Ben tried to toss a ping-pong ball in a cup using 10 trials, out of which he succeeded 4 times. 

P(win) $= \frac{Number of success}{Number of trials}$

             $= \frac{4}{10}$

             $= \frac{2}{5}$

Example 2: Two students are playing a game of die. They want to know how many times they land on 2 on the dice if the die is rolled 20 times in a row. 

The experimental probability of rolling a 2 

$= \frac{Number of times 2 appeared}{Number of trials}$

$= \frac{5}{20}$

$= \frac{1}{4}$

1. Probability of an event always lies between 0 and 1.

2. You can also express the probability as a decimal and a percentage.

Experimental probability is a probability that is determined by the results of a series of experiments. Learn more such interesting concepts at SplashLearn .

1. Leo tosses a coin 25 times and observes that the “head” appears 10 times. What is the experimental probability of getting a head?

 P(Head) $= \frac{Number of times heads appeared}{Total number of trials}$

               $= \frac{10}{25}$

               $= \frac{2}{5}$

               $= 0.4$

2. The number of cakes a baker makes per day in a week is given as 7, 8, 6, 10, 2, 8, 3. What is the probability that the baker makes less than 6 cakes the next day?

Solution: 

Number of cakes baked each day in a week $= 7, 8, 6, 10, 2, 8, 3$

Out of 7 days, there were 2 days (highlighted in bold) on which the baker made less than 6 cookies.

P$(< 6 $cookies$) = \frac{2}{7}$

3. The chart below shows the number of times a number was shown on the face of a tossed die. What was the probability of getting a 3 in this experiment?

Number of times 3 showed $= 7$

Number of tosses $= 30$

P(3) $= \frac{7}{30}$

4. John kicked a ball 20 times. He kicked 16 field goals and missed 4 times . What is the experimental probability that John will kick a field goal during the game?

Solution:  

John succeeded in kicking 16 field goals. He attempted to kick a field goal 20 times. 

So, the number of trials $= 20$

John’s experimental probability of kicking a field goal $= \frac{Successful outcomes} {Trials attempted} = \frac{16}{20}$ 

$= \frac{4}{5}$

$= 0.8$ or $80%$

5. James recorded the color of bikes crossing his street. Of the 500 bikes, 10 were custom colors, 100 were white, 50 were red, 120 were black, 100 were silver, 60 were blue, and 60 were gray. What is the probability that the car crossing his street is white?

Number of white bikes $= 100$ 

Total number of bikes $= 500$

P(white bike) $=  \frac{100}{500} = \frac{1}{5}$

Attend this quiz & Test your knowledge.

In a class, a student is chosen randomly in five trials to participate in 5 different events. Out of chosen students, 3 were girls and 2 were boys. What is the experimental probability of choosing a boy in the next event?

A manufacturer makes 1000 tablets every month. after inspecting 100 tablets, the manufacturer found that 30 tablets were defective. what is the probability that you will buy a defective tablet, the 3 coins are tossed 1000 times simultaneously and we get three tails $= 160$, two tails $= 260$, one tail $= 320$, no tails $= 260$. what is the probability of occurrence of two tails, the table below shows the colors of shirts sold in a clothing store on a particular day and their respective frequencies. use the table to answer the questions that follow. what is the probability of selling a blue shirt.

Jason leaves for work at the same time each day. Over a period of 327 working days, on his way to work, he had to wait for a train at the railway crossing for 68 days. What is the experimental probability that Jason has to wait for a train on his way to work?

What is the importance of experimental probability?

Experimental probability is widely used in research and experiments in various fields, such as medicine, social sciences, investing, and weather forecasting.

Is experimental probability always accurate?

Predictions based on experimental probability are less reliable than those based on theoretical probability.

Can experimental probability change every time the experiment is performed?

Since the experimental probability is based on the actual results of an experiment, it can change when the results of an experiment change.

What is theoretical probability?

The theoretical probability is calculated by finding the ratio of the number of favorable outcomes to the total number of probable outcomes.

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Experimental Probability

The outcome of an actual experiment involving numerous trials is called experimental probability. Learn more about exper imental probability and its properties in this article. ...Read More Read Less

About Experimental Probability

Defining Probability

How precisely do we define experimental probability, formulation.

• Solved Examples
• Frequently Asked Questions

The mathematics of chance is known as probability (p). The probability of occurrence of an event (E) is revealed by probability.

The probability of an event can be expressed as a number between 0 and 1. 

The likelihood of an impossibility is zero. A probability between 0 and 1 can be attributed to any other events that fall in between these two extremes. Experimental probability is the probability that is established based on the outcomes of an experiment. The term ‘ empirical probability ’ is also used for the same concept.

A probability that has been established by a series of tests is called an experimental probability. To ascertain their possibility, a random experiment is conducted and iterated over a number of times; each iteration is referred to as a trial . 

The goal of the experiment is to determine the likelihood of an event occurring or not. 

It could involve spinning a spinner, tossing a coin, or using a dice. The probability of an event is defined mathematically as the number of occurrences of the event divided by the total number of trials.

The number of times an event occurred during the experiment divided by all the times the experiment was run is known as the experimental probability of that event. Each potential result is unknown, and the collection of all potential results is referred to as the sample space . 

Experimental probability is calculated using the following formula:

\(P(E)=\frac{Number~of~times~an~event~occurred~during~an~experiment}{The~total~number~of~times~the~experiment~was~conducte}\)

\(P(E)=\frac{n(E)}{n(S)}\)

n(E) = Number of events occurred

n(S) = Number of sample space

Solved Experimental Probability Examples

Example 1: The owner of a cake store is curious about the percentage of sales of his new gluten-free cupcake line. He counts the number of cakes that were sold on one day of the week, Monday, where he sold 30 regular and 70 gluten free cakes. Calculate the probability in this case.

According to the details in the question, the number of gluten free cakes is n(E) = 70 cakes.

Total number of cakes n(S) = 30 + 70 = 100 cakes.

Substituting these values in the formula.

\(P(E)=\frac{n(E)}{n(S)}\) = \(\frac{70}{100}\)   = 0.7 = 70%

Hence, the owner of the cake store finds that the gluten-free cupcakes will probably make up 70% of his weekly sales.

Example 2: A baseball manager is interested to know the probability that a prospective new player will hit a home run in the game’s first at-bat. The player has 11 home runs in 1921 games throughout his career. Calculate the probability of the player hitting a home run.

The data provided is, the player has hit 11 home runs, n(E) = 11

Total number of games, n(s) = 1921 games.

\(P(E)=\frac{n(E)}{n(S)}\) = \(\frac{11}{1921}\)   = 0.005726 = 0.5726%

He will therefore have a 0.5726 percent chance of hitting a home run in his first at-bat.

Example 3: A vegetable gardener is checking the likelihood that a fresh bitter gourd seed would germinate. He plants 100 seeds, and 57 of them sprout new plants. Calculate the probability in this scenario.

According to the question, the number of bitter gourd plants that sprouted is n(E) = 57.

Total number of seeds sown, n(S) = 100 seeds.

\(P(E)=\frac{n(E)}{n(S)}\) = \(\frac{57}{100}\)   = 0.57 = 57%

Hence, the probability that a new bitter gourd seed will be sprout is 57% . 

Example 4: Joe’s Bagel Shop sold 26 bagels in one day, 9 of which were raisin bagels. Calculate the percentage of raisin bagels that will be sold the following day using experimental probability.

As stated in the question, the number of raisin bagels, n(E) = 9.

Total number of bagels Joe sold, n(s) = 26 .

\(P(E)=\frac{n(E)}{n(S)}\) = \(\frac{9}{26}\)   = 0.346 = 34.6%

As a result, there is a 34.6 percent chance that Joe will sell raisin bagels the following day.

Do you simplify the probabilities of experiments?

Yes, the ratio obtained is simplified after the ratio between the frequency of the occurrence and the total number of trials is determined.

Which type of probability — theoretical or experimental — is more accurate?

Compared to experimental probability, theoretical probability is more precise. Only if there are more trials, then the results of experimental probability will be close to the results from theoretical probability.

How can experimental probability be calculated?

Actual tests and recordings of events serve as the foundation for calculating the experimental probability of an event. It is determined by dividing the total number of trials by the number of times an event occurred.

What is the chance of getting a 1 when you throw a dice?

A ‘1’ has a 1/6 experimental probability of rolling. Six faces, numbered from 1 to 6, make up a dice. Any number between 1 and 6 can be obtained by rolling the dice, and the likelihood of getting a 1 is equal to the ratio of favorable results to all other potential outcomes, or 1/6.

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