# Comparing experimental and theoretical probability

##### Intros
###### Lessons
1. Experimental probability VS. Theoretical probability
##### Examples
###### Lessons
1. Bon rolled a 6-sided dice 16 times and recorded the outcomes shown. Does the chart provide information about experimental or theoretical probability? Explain how you know.
 Die Outcome Experimental results 1 II 2 IIII 3 I 4 III 5 IIIII 6 I
1. Jessie flips two coins 20 times to determine the experimental probability of landing on heads versus tails. Here are his results.
 Coin Outcome Experimental Results H, H IIII H, T IIIII T, H IIIIII T, T IIIII
1. What is the experimental probability of both coins landing on heads?
2. Calculate the theoretical probability of both coins landing on heads.
3. Compare the theoretical probability and experimental probability.
4. What can Jessie do to decrease the difference between the theoretical probability and experimental probability?
###### Topic Notes
In this section, we compare experimental probability and theoretical probability. Experimental probability is the probability of an event occurring based on experimental results. In contrast, theoretical probability is the expected probability of an event occurring. In previous sections, we practiced calculating theoretical probability. In this section, we examine a given chart to determine whether it provides information about experimental or theoretical probability. Also, we practice interpreting tables of experimental results and using these tables to calculate the experimental and theoretical probabilities of outcomes occurring.

## Introduction

Comparing experimental and theoretical probability is a fundamental concept in statistics that helps us understand the relationship between predicted outcomes and real-world results. Our introduction video provides a comprehensive overview of this topic, serving as an essential starting point for students and enthusiasts alike. This article delves deeper into the subject, exploring the definitions, key differences, and practical applications of both theoretical and experimental probability. Theoretical probability is based on mathematical calculations and assumes ideal conditions, while experimental probability relies on actual observations and data collection. Understanding the interplay between these two types of probability is crucial for making informed decisions in various fields, from scientific research to everyday life. As we progress through this article, we'll examine how these concepts are applied in real-world scenarios, highlighting their importance in fields such as finance, weather forecasting, and quality control. By the end, you'll have a solid grasp of how theoretical and experimental probability complement each other in our quest to understand and predict outcomes.

## Defining Theoretical and Experimental Probability

Probability is a fundamental concept in mathematics that helps us understand the likelihood of events occurring. Two key types of probability are theoretical probability and experimental probability. Understanding these concepts is crucial for anyone studying statistics or working with data analysis.

Theoretical probability, also known as classical probability, is based on mathematical calculations and logical reasoning. It represents the expected likelihood of an event occurring under ideal conditions. This type of probability is determined by analyzing the possible outcomes of a situation and calculating the ratio of favorable outcomes to the total number of possible outcomes. Theoretical probability is often expressed as a fraction, decimal, or percentage.

On the other hand, experimental probability, sometimes called empirical probability, is derived from actual trials or experiments. It represents the observed frequency of an event occurring in real-world situations. Experimental probability is calculated by conducting multiple trials and recording the number of times a specific outcome occurs, then dividing that number by the total number of trials. Like theoretical probability, it can be expressed as a fraction, decimal, or percentage.

To illustrate these concepts, let's consider the classic example of a coin flip. In theory, a fair coin has two equally likely outcomes: heads or tails. The theoretical probability of getting heads on a single flip is 1/2 or 50%. This calculation is based on the assumption that the coin is perfectly balanced and the flip is unbiased.

However, when we actually flip a coin multiple times, we might observe slightly different results. For instance, if we flip a coin 100 times and get 48 heads, the experimental probability of getting heads would be 48/100 or 48%. This result is based on the actual outcomes of the experiment rather than theoretical calculations.

Calculating theoretical probability involves identifying all possible outcomes and determining the number of favorable outcomes. For example, when rolling a six-sided die, the theoretical probability of rolling an even number is 3/6 or 1/2, as there are three favorable outcomes (2, 4, and 6) out of six possible outcomes.

Experimental probability calculations require conducting multiple trials and recording the results. For instance, if we roll a die 200 times and observe 98 even numbers, the experimental probability would be 98/200 or 49%. This approach provides a real-world estimate of the probability based on observed data.

It's important to note that as the number of trials in an experiment increases, the experimental probability tends to converge towards the theoretical probability. This phenomenon is known as the law of large numbers. However, due to various factors such as imperfections in objects or experimental conditions, there may always be slight differences between theoretical and experimental probabilities.

Understanding both theoretical and experimental probability is essential in various fields, including statistics, science, and data analysis. Theoretical probability provides a baseline expectation, while experimental probability offers insights into real-world outcomes. By comparing these two types of probability, researchers can identify discrepancies, validate theories, and make informed decisions based on both mathematical models and empirical evidence.

## Comparing Theoretical and Experimental Probability

Understanding the key differences between theoretical and experimental probability is crucial in the field of statistics and probability theory. Theoretical probability, also known as classical probability, is based on mathematical calculations and assumes ideal conditions. It represents the expected likelihood of an event occurring in the long run. On the other hand, experimental probability, or empirical probability, is derived from actual observations and experiments conducted in real-world scenarios.

The main distinction between these two types of probability lies in their approach and application. Theoretical probability is calculated using mathematical formulas and logical reasoning, without the need for physical experiments. It assumes that all outcomes are equally likely and that the conditions remain constant. For instance, when considering a fair six-sided die, the theoretical probability of rolling any number is 1/6, as there are six equally possible outcomes.

Experimental probability, however, is determined by conducting actual trials or experiments and recording the results. It takes into account real-world factors and variations that may influence the outcomes. This type of probability is based on the frequency of occurrences in a given number of trials. For example, if you roll a die 100 times and observe that the number 4 appears 18 times, the experimental probability of rolling a 4 would be 18/100 or 0.18.

One of the most intriguing aspects of probability is that experimental results might not always match theoretical expectations, especially when dealing with small sample sizes. This discrepancy can be attributed to several factors. Firstly, random chance plays a significant role in small samples, leading to potential deviations from the expected outcomes. Secondly, real-world conditions may introduce subtle biases or imperfections that are not accounted for in theoretical calculations.

To illustrate this point, let's consider the die rolling example from the video. In theory, each number on a fair die has an equal probability of 1/6 or approximately 0.167. However, if you were to roll the die only 10 times, you might observe results that differ significantly from this theoretical expectation. For instance, you might roll a 3 four times out of 10 rolls, giving an experimental probability of 0.4 for rolling a 3. This result clearly deviates from the theoretical probability of 0.167.

The reason for this discrepancy lies in the small sample size. With only 10 rolls, random fluctuations have a more pronounced effect on the results. It's important to note that such deviations do not necessarily indicate that the die is unfair or that the theoretical probability is incorrect. Instead, it highlights the impact of sample size on experimental probability.

As the number of trials increases, a fascinating phenomenon occurs: experimental probability tends to approach theoretical probability. This concept is known as the Law of Large Numbers. In our die rolling example, if you were to increase the number of rolls to 1,000 or even 10,000, you would likely observe that the experimental probabilities for each number start to converge towards the theoretical probability of 1/6.

This convergence happens because larger sample sizes provide more opportunities for random fluctuations to balance out. With more trials, the impact of short-term variations diminishes, and the overall pattern begins to align more closely with the expected theoretical outcomes. It's important to note that while experimental probability may approach theoretical probability with a large number of trials, it may never exactly match it due to real-world factors and inherent randomness.

Understanding the relationship between theoretical and experimental probability is crucial in various fields, including scientific research, quality control, and data analysis. It helps researchers and analysts interpret results more accurately, especially when dealing with small sample sizes. By recognizing the potential for discrepancies between theoretical expectations and experimental outcomes, one can avoid drawing hasty conclusions and instead consider the role of sample size and other influencing factors.

In conclusion, while theoretical and experimental probability offer different approaches to understanding the likelihood of events, they are complementary in nature. Theoretical probability provides a foundation for expectations, while experimental probability offers insights into real-world outcomes. By considering both aspects and understanding their relationship, we can gain a more comprehensive and nuanced understanding of probability in various contexts.

## Calculating and Interpreting Probabilities

Understanding how to calculate both theoretical and experimental probabilities is crucial in various fields, from statistics to everyday decision-making. This guide will walk you through the process step-by-step, using practical examples to illustrate the concepts.

### How to Calculate Theoretical Probability

Theoretical probability is based on the assumption that all outcomes are equally likely. To calculate it:

1. Identify the total number of possible outcomes.
2. Determine the number of favorable outcomes (those that meet your criteria).
3. Divide the number of favorable outcomes by the total number of possible outcomes.

Example: Finding the probability of rolling an even number on a six-sided die.

• Total outcomes: 6 (1, 2, 3, 4, 5, 6)
• Favorable outcomes: 3 (2, 4, 6)
• Probability = 3/6 = 1/2 or 0.5 or 50%

### How to Find Experimental Probability

Experimental probability is based on actual trials or observations. To calculate it:

1. Conduct a series of trials or observations.
2. Count the number of times the desired outcome occurs.
3. Divide the number of favorable outcomes by the total number of trials.

Example: Rolling a die 100 times and counting even numbers.

• Total rolls: 100
• Number of even rolls: 48
• Experimental probability = 48/100 = 0.48 or 48%

### Using Frequency Charts for Experimental Probability

Frequency charts for probability are valuable tools for determining experimental probability:

1. Create a chart listing all possible outcomes.
2. Tally the occurrences of each outcome during your trials.
3. Calculate the probability by dividing each outcome's frequency by the total number of trials.

Example: A frequency chart for probability for 50 coin flips might show:

• Heads: 27 (Probability: 27/50 = 0.54)
• Tails: 23 (Probability: 23/50 = 0.46)

### Interpreting Probability Results

Understanding what probability results mean is crucial:

• A probability of 0 means the event will never occur.
• A probability of 1 (or 100%) means the event will always occur.
• A probability of 0.5 (or 50%) means the event is equally likely to occur or not occur.

In practical terms, a 60% chance of rain means that, under similar conditions, it rains 60 out of 100 times. It doesn't guarantee rain but suggests it's more likely than not.

### Comparing Theoretical and Experimental Probabilities

Often, theoretical and experimental probabilities differ slightly:

• Theoretical probability assumes ideal conditions.
• Experimental probability reflects real-world variations.
• As the number of trials increases, experimental probability typically approaches theoretical probability.

### Practical Applications

Understanding probability is essential in various fields:

• Weather forecasting: Predicting the likelihood of specific weather conditions.

## Advantages and Limitations of Theoretical and Experimental Probability

Probability is a fundamental concept in mathematics and statistics, with two primary approaches: theoretical and experimental probability. Each method has its own set of advantages and limitations, making them suitable for different situations. Understanding these differences is crucial for accurately predicting outcomes and making informed decisions.

Theoretical probability, also known as classical probability, is based on mathematical calculations and assumes ideal conditions. Its main advantage lies in its ability to provide precise predictions for well-defined scenarios. For instance, when calculating the probability of rolling a specific number on a fair die, theoretical probability offers an exact mathematical solution. This approach is particularly useful in games of chance, risk assessment, and scientific modeling where conditions can be controlled or standardized.

However, theoretical probability has limitations. It often assumes perfect conditions that may not exist in the real world. For example, a coin toss is theoretically a 50/50 chance, but factors like air resistance, the tosser's technique, or an uneven surface can influence the outcome. In complex real-world scenarios, theoretical probability may oversimplify the situation, leading to inaccurate predictions.

On the other hand, experimental probability, also called empirical probability, is based on actual observations and data collection. One of the key advantages of experimental probability is its ability to account for real-world factors that theoretical models might miss. This approach is particularly valuable in situations where conditions are not ideal or when multiple variables are at play. For instance, in weather forecasting, experimental probability based on historical data and current observations often provides more accurate predictions than purely theoretical models.

A significant advantage that experimental probability has over theoretical probability is its capacity to reveal unexpected patterns or influences that might not be apparent in a theoretical model. By conducting repeated trials and collecting data, researchers can uncover factors affecting outcomes that weren't initially considered. This makes experimental probability invaluable in fields like medicine, social sciences, and economics, where human behavior and complex interactions play a crucial role.

However, experimental probability is not without its limitations. It requires a large number of trials to produce reliable results, which can be time-consuming and costly. The accuracy of experimental probability also depends on the quality of data collection and the representativeness of the sample. Biases in sampling or measurement errors can lead to skewed results. Additionally, rare events may be underrepresented in experimental data, potentially leading to inaccurate probability estimates for uncommon occurrences.

In practice, the choice between theoretical and experimental probability often depends on the specific situation and available resources. Theoretical probability is preferred in scenarios where conditions can be well-defined and controlled, such as in casino games or basic physics problems. It's also useful for initial predictions or when conducting experiments is impractical or impossible.

Experimental probability, however, shines in complex, real-world applications where multiple factors influence outcomes. It's particularly valuable in fields like epidemiology, market research, and environmental science. In these areas, the ability to account for unforeseen variables and real-world conditions makes experimental probability a more reliable approach.

Ultimately, the most effective approach often involves combining both methods. Theoretical probability can provide a baseline expectation, while experimental probability can refine and validate these predictions in real-world contexts. This complementary use of both approaches allows for more robust and accurate probability assessments, leading to better decision-making and more reliable predictions across various fields of study and practical applications.

## Practical Applications and Examples

Theoretical and experimental probability play crucial roles in various fields, from statistics and science to everyday decision-making. Let's explore some real-world examples and applications of these concepts, along with exercises to help you practice calculating probabilities.

### Weather Forecasting

Meteorologists use both theoretical and experimental probability to predict weather patterns. Theoretical probability is applied when analyzing historical data and atmospheric models, while experimental probability comes into play as they gather real-time data from weather stations and satellites. For example, a meteorologist might state that there's a 70% chance of rain tomorrow based on a combination of these probabilities.

### Sports Statistics

In sports analytics, theoretical probability is used to calculate the likelihood of certain outcomes based on past performance and team statistics. Experimental probability is then applied as games are played and new data is collected. For instance, a basketball player's free throw percentage is an example of experimental probability, while the theoretical probability of making a three-pointer might be calculated based on court position and defensive pressure.

### Medical Research

In clinical trials, researchers use theoretical probability to design studies and determine sample sizes. As the trial progresses, they collect data and calculate experimental probabilities to assess the effectiveness of treatments. For example, the theoretical probability of a new drug's success might be estimated based on its chemical properties, while the experimental probability is determined through actual patient outcomes.

### Insurance Industry

Insurance companies rely heavily on probability to set premiums and assess risk. They use theoretical probability models based on demographic data and historical trends, then adjust these models with experimental probability as they gather data from their policyholders. For instance, the probability of a car accident for a specific age group might be theoretically calculated, then refined with actual claim data.

### Quality Control in Manufacturing

Manufacturers use probability to maintain product quality. Theoretical probability helps in setting quality standards, while experimental probability is used in sampling and testing products. For example, a theoretical model might predict the probability of defects in a production line, which is then compared to the actual defect rate found through quality control checks.

### Financial Markets

Investors and financial analysts use probability to assess market trends and make investment decisions. Theoretical models predict stock performance based on economic indicators, while experimental probability is derived from actual market data. For instance, the probability of a stock price increase might be theoretically calculated, then compared to its actual performance over time.

### Exercises

1. Theoretical Probability: A fair six-sided die is rolled. Calculate the probability of rolling an even number.

2. Experimental Probability: In a bag of 100 marbles, you draw marbles 50 times with replacement and find that you've drawn a red marble 15 times. What is the experimental probability of drawing a red marble?

3. Combined Exercise: A weather forecast predicts a 30% chance of rain based on historical data. Over the next 10 days, it actually rains on 4 days. Compare the theoretical and experimental probabilities.

4. Real-world Application: A basketball player has made 80 out of 100 free throws this season. What is the experimental probability of them making their next free throw? How might this differ from the theoretical probability based on their career average?

By understanding and applying both theoretical and experimental probability, we can make more informed decisions in various aspects of life. Whether it's predicting outcomes, assessing risks, or analyzing data, probability serves as a powerful tool across numerous fields. Practice with these concepts will enhance your ability to interpret and use probability in real-world situations.

## Conclusion

In this article, we've explored the fundamental concepts of theoretical and experimental probability, building upon the foundation laid in the introductory video. We've discussed the key differences between these two approaches and their practical applications in various fields. Understanding both theoretical calculations and real-world experimental results is crucial for a comprehensive grasp of probability. The examples provided illustrate how these concepts apply to everyday situations, from coin tosses to complex scientific experiments. As you continue your studies, remember to apply these principles to enhance your critical thinking and decision-making skills. Probability theory is not just an academic exercise but a powerful tool for navigating uncertainties in life. We encourage you to delve deeper into this fascinating subject, experiment with probability scenarios, and share your insights with others. For further exploration, consider joining online forums, attending workshops, or exploring advanced probability courses to expand your knowledge and practical skills in this essential mathematical field.

### Example:

Jessie flips two coins 20 times to determine the experimental probability of landing on heads versus tails. Here are his results.

 Coin Outcome Experimental Results H, H IIII H, T IIIII T, H IIIIII T, T IIIII
What is the experimental probability of both coins landing on heads?

#### Step 1: Understanding the Problem

First, we need to understand the problem. Jessie flips two coins 20 times to determine the experimental probability of landing on heads versus tails. The results are given in a table format, showing the outcomes and the number of times each outcome occurred. Our task is to find the experimental probability of both coins landing on heads (H, H).

#### Step 2: Analyzing the Results

Next, we analyze the results provided in the table. The table shows the following outcomes and their respective frequencies:

• H, H: 4 times
• H, T: 5 times
• T, H: 6 times
• T, T: 5 times
We can see that the outcome of both coins landing on heads (H, H) occurred 4 times out of the 20 trials.

#### Step 3: Calculating the Experimental Probability

To calculate the experimental probability, we use the formula:

Experimental Probability = (Number of Successful Outcomes) / (Total Number of Trials)

In this case, the number of successful outcomes (both coins landing on heads) is 4, and the total number of trials is 20. Therefore, the experimental probability is:

Experimental Probability = 4 / 20

#### Step 4: Simplifying the Fraction

To simplify the fraction, we find the greatest common divisor (GCD) of the numerator and the denominator. The GCD of 4 and 20 is 4. We divide both the numerator and the denominator by their GCD:

4 ÷ 4 = 1
20 ÷ 4 = 5

Therefore, the simplified fraction is:

1 / 5

#### Step 5: Interpreting the Result

The experimental probability of both coins landing on heads is 1 out of 5, or 1/5. This means that, based on Jessie's experiment, there is a 20% chance (since 1/5 = 0.20) that both coins will land on heads in any given trial.

### FAQs

#### 1. What is the difference between theoretical and experimental probability?

Theoretical probability is based on mathematical calculations and assumes ideal conditions, while experimental probability is derived from actual observations and data collection. Theoretical probability predicts outcomes based on logical reasoning, whereas experimental probability reflects real-world results.

#### 2. How do you calculate theoretical probability?

To calculate theoretical probability, use the formula: P(event) = (number of favorable outcomes) / (total number of possible outcomes). For example, the theoretical probability of rolling a 3 on a fair six-sided die is 1/6, as there is one favorable outcome out of six possible outcomes.

#### 3. What is an example of experimental probability?

An example of experimental probability is flipping a coin 100 times and observing 52 heads. The experimental probability of getting heads would be 52/100 = 0.52 or 52%. This result is based on actual observations rather than theoretical calculations.

#### 4. Why might theoretical and experimental probabilities differ?

Theoretical and experimental probabilities may differ due to factors such as sample size, random variation, and real-world conditions that aren't accounted for in theoretical models. As the number of trials increases, experimental probability tends to converge towards theoretical probability.

#### 5. Which is more reliable, theoretical or experimental probability?

Neither is inherently more reliable; they serve different purposes. Theoretical probability provides a baseline expectation, while experimental probability reflects real-world outcomes. In practice, combining both approaches often yields the most comprehensive understanding of probability in a given situation.

### Prerequisite Topics

Before delving into the intricacies of comparing experimental and theoretical probability, it's crucial to understand the foundational concepts that support this topic. One of the most important prerequisite topics is influencing factors in data collection. This fundamental concept plays a significant role in shaping our understanding of probability and its practical applications.

Understanding the influencing factors in data collection is essential when comparing experimental and theoretical probability. These factors directly impact the quality and reliability of the data we use to calculate experimental probability. For instance, when conducting experiments to determine probability, the methods used for data collection can significantly influence the outcomes. Biases, sample size, and data collection techniques all play crucial roles in shaping the experimental results.

Moreover, the concept of data collection is intrinsically linked to the process of gathering empirical evidence, which is the cornerstone of experimental probability. By understanding how various factors can influence data collection, students can better appreciate the potential discrepancies between experimental and theoretical probability.

For example, when comparing the theoretical probability of rolling a six on a die (1/6) with the experimental probability derived from actual rolls, the influencing factors in data collection become evident. The number of rolls (sample size), the randomness of the rolling technique, and even the physical characteristics of the die can all affect the experimental results. These factors highlight the importance of understanding data collection principles when interpreting and comparing probabilities.

Furthermore, grasping the concepts related to influencing factors in data collection helps students develop a critical eye when analyzing probability studies. It enables them to identify potential sources of error or bias in experimental setups, leading to a more nuanced understanding of the relationship between theoretical and experimental probabilities.

In conclusion, the prerequisite topic of influencing factors in data collection serves as a crucial foundation for comparing experimental and theoretical probability. It equips students with the necessary tools to critically evaluate data, understand the limitations of experimental results, and appreciate the complexities involved in real-world probability calculations. By mastering this prerequisite, students will be better prepared to tackle the challenges of comparing different types of probabilities and apply their knowledge in various statistical contexts.