Experimental Probability – Definition with Examples
Created on Jan 09, 2024
Updated on January 9, 2024
At Brighterly, we believe that a solid understanding of mathematics can empower our children to do great things. That’s why we’re committed to making complex math concepts accessible, engaging, and fun for all children. Among the myriad of mathematical topics we cover, one of the more practical, yet fascinating, is experimental probability.
Experimental probability is like the bridge between math and the real world, offering a handson approach to understanding likelihood and chance. It’s all about observation, data collection, and making sense of the patterns that emerge. Experimental probability takes us beyond the theoretical and into the empirical, providing our children with a richer, fuller understanding of how probability works.
What Is Experimental Probability?
Experimental probability is a concept that children often encounter in their mathematical journey, and it provides a fantastic way to understand how probability works in the real world. It is a type of probability that we calculate based on the outcomes of an experiment or activity, as opposed to theoretical probability which we calculate using mathematical principles. It’s all about doing rather than just thinking.
Imagine you’re flipping a coin. The theoretical probability of getting a heads or tails is 50%, or 0.5, because these are the only two possible outcomes. However, if you flip the coin 10 times and get 7 heads and 3 tails, the experimental probability of getting heads is 70% (or 0.7), and for tails, it’s 30% (or 0.3). This is because experimental probability depends on the actual results of the experiment.
Definition of Experimental Probability
The definition of experimental probability is the ratio of the number of times an event occurs to the total number of trials or times the activity is performed. It is calculated after conducting an experiment or activity, and can often differ from theoretical probability because of the variability and unpredictability of realworld events.
Calculating Experimental Probability
The process of calculating experimental probability involves two steps: conducting the experiment to gather data, and then using that data to calculate the probability. The formula for calculating experimental probability is:
P(E) = Number of times event E occurs / Total number of trials
For example, if you roll a dice 60 times, and the number 4 comes up 15 times, the experimental probability of rolling a 4 is calculated as 15 (the number of times 4 occurs) divided by 60 (the total number of trials), which equals 0.25, or 25%.
Examples of Experimental Probability
To better understand this concept, let’s explore some realworld examples of experimental probability:

In a bag of 30 marbles, 10 are blue, 10 are green, and 10 are red. If you randomly pick a marble 30 times, replacing the marble each time, and you get 12 blue, 8 green, and 10 red marbles, the experimental probability for each color would be calculated as follows:
 Blue: 12/30 = 0.4 or 40%
 Green: 8/30 = 0.267 or 26.7%
 Red: 10/30 = 0.333 or 33.3%

In a deck of 52 playing cards, if you draw a card 52 times, replacing the card each time, and you draw a heart 15 times, the experimental probability of drawing a heart is 15/52 = 0.288 or 28.8%.
Properties of Experimental Probability
Experimental probability, as with any type of probability, possesses some key properties. It will always be a value between 0 and 1 (or 0% and 100% when expressed as a percentage). This makes sense, as it’s impossible for an event to occur less than 0 times (probability = 0), or more times than the total number of trials (probability = 1).
Another key property is that the sum of the probabilities of all possible outcomes will equal 1. For example, in our earlier coin flipping example, the sum of the experimental probabilities for getting heads (0.7) and tails (0.3) equals 1.
Key Factors Affecting Experimental Probability
The key factor affecting experimental probability is the number of trials. In general, the more trials are performed, the closer the experimental probability gets to the theoretical probability. This principle is known as the Law of Large Numbers.
Other factors that can affect experimental probability include inaccuracies in data collection and environmental variables, such as the fairness of a coin or die, the method of drawing cards, and so on.
Difference Between Experimental and Theoretical Probability
The main difference between experimental and theoretical probability lies in their calculation methods. Theoretical probability is determined mathematically, using the known outcomes of an event, while experimental probability is determined empirically, using data from actual trials of the event.
In theory, a coin has a 50% chance of landing on heads, but in an experiment, it might not. Over the long run, the experimental probability will likely get closer to the theoretical probability, thanks to the Law of Large Numbers.
Formulas for Calculating Experimental Probability
As mentioned earlier, the formula for calculating experimental probability is straightforward:
P(E) = Number of times event E occurs / Total number of trials
Here, ‘P(E)’ represents the probability of event E occurring.
Writing Formulas for Experimental Probability
Let’s get into the details of how to write formulas for experimental probability. For any given event E, you can express the experimental probability of that event occurring as a fraction, decimal, or percentage using the aforementioned formula. Just remember to divide the number of times the event occurred by the total number of trials.
For example, if you’re trying to find the experimental probability of drawing a heart from a deck of cards and you drew a heart 13 times out of 52 trials, you’d write it as follows:
P(Heart) = 13/52 ≈ 0.25 = 25%
Use Cases of Experimental Probability in Real Life
Experimental probability finds its use in various reallife scenarios, from games and sports to weather forecasting and medical research. For example, predicting the outcome of a football game based on past performances is a use of experimental probability. Likewise, weather forecasts use data from previous years to predict the likelihood of certain weather conditions. Experimental probability is also used in clinical trials to determine the effectiveness of a new drug or treatment.
Practice Problems on Experimental Probability
To fully understand experimental probability, it’s helpful to solve some practice problems. Try the following scenarios:
 You toss a coin 50 times and get heads 29 times. What is the experimental probability of getting heads?
 You draw a card from a deck of 52 cards 100 times and draw a queen 22 times. What is the experimental probability of drawing a queen?
 You roll a die 200 times and roll a 5, 40 times. What is the experimental probability of rolling a 5?
Conclusion
In conclusion, experimental probability offers a practical and exciting way for children to understand the concept of probability and chance. Through experiments and observations, children can learn not just how to calculate the likelihood of an event, but also develop an intuitive understanding of probability.
At Brighterly, we encourage our young learners to immerse themselves in the world of experimental probability and explore its numerous applications in reallife situations. From games and sports to weather forecasting and medical research, experimental probability has vast realworld significance. Remember, with more trials, the experimental probability tends to converge with the theoretical probability, making it a valuable tool in understanding uncertainty and making predictions.
Frequently Asked Questions on Experimental Probability
What is the formula for experimental probability?
The formula for experimental probability is: P(E) = Number of times event E occurs / Total number of trials. Here, P(E) stands for the probability of event E, which could be any event you’re examining. This formula is straightforward to use, and it allows you to compute the experimental probability accurately using your collected data.
How is experimental probability calculated?
Experimental probability is calculated by carrying out an experiment and recording the outcomes. The number of times a particular event occurs is then divided by the total number of trials conducted. For example, if you roll a dice 100 times and the number 4 comes up 20 times, the experimental probability of rolling a 4 would be 20/100 = 0.20, or 20%.
What is the difference between experimental and theoretical probability?
Theoretical probability and experimental probability differ in the ways they are determined. Theoretical probability is derived using mathematical principles, considering all possible outcomes of an event. For instance, when flipping a fair coin, the theoretical probability of getting a head is 50% since there are two equally likely outcomes – heads and tails. On the other hand, experimental probability is calculated based on actual experiments or trials. If you flip the same coin 100 times and get heads 60 times, the experimental probability of getting heads would be 60/100 = 0.60, or 60%. Over time, with a large number of trials, the experimental probability will tend to get closer to the theoretical probability. This is a consequence of the Law of Large Numbers.