Chapter 21

FREQUENCY DISTRIBUTIONS: HOW THEY ARTICULATE WITH AI AND MACHINES LEARN FROM NUMBERS

Key to data analysis and intelligent decision-making.

Frequency distributions are   a way of organizing data to understand  how often certain values ​​or ranges of values ​​occur . They are particularly used in descriptive statistics to summarize large amounts of data in a table or graph.
Common types include:
Absolute frequency : The number of times a value appears.
Relative frequency : The percentage of the total that each value represents.
Cumulative frequency : The cumulative sum of frequencies up to a given point.

Artificial intelligence, especially  machine learning , relies heavily on data analysis. This is where frequency distributions come in:

Data preprocessing:
Before training an AI model, it is essential to explore and understand the data. Distributions help to:
detect  outliers , find  dominant variables  , and identify  class imbalances (e.g., if there is much more data from one category than another).
In classification (e.g., recognizing spam/non-spam emails), if a class occurs more frequently, the model may become biased; techniques such as:
Frequency-based undersampling or oversampling , Class weighting ,  visualization, and interpretation . AIs that generate automatic reports use distributions to: generate explanatory graphs , produce automated narratives ("70% of customers are between 25 and 35 years old").

Frequency-dependent algorithms:  Some models, such as  Naive Bayes , directly use the frequency of the data (frequency-based conditional probabilities).
Real-time analytics:  AI systems that monitor processes (such as sensors or social networks) create dynamic distributions to detect abnormal behavior in real time.

Practical reflection:  Frequency distributions are  the statistical basis for many intelligent decisions . When combined with AI, they allow machines to:
Better understand context, learn from historical data, and improve their predictive and recommendation capabilities.

Clear and useful article  on  frequency distributions and their relationship with artificial intelligence :

In a world where data is considered the new oil, understanding how it is distributed becomes key to making intelligent decisions. Frequency distributions, a basic statistical tool, allow for clear visualization and analysis of how data behaves. When combined with artificial intelligence (AI), a powerful synergy emerges that drives effective analysis and prediction.

Frequency distributions are   a way of organizing data to understand  how often certain values ​​or ranges of values ​​occur . They are particularly used in descriptive statistics to summarize large amounts of data in a table or graph.

Simple example:

If we have the age data of 10 people:
[20, 22, 20, 21, 20, 22, 23, 21, 22, 23]

Artificial intelligence, especially  machine learning , relies heavily on data analysis. This is where frequency distributions come in:

Data preprocessing:  Before training an AI model, it's essential to explore and understand the data. Distributions help detect  outliers , find  dominant variables  , and identify  class imbalances (e.g., if there is significantly more data in one category than another).
Classification balancing:  (e.g., recognizing spam/non-spam emails): If one class occurs more frequently, the model may become biased. Techniques such as:
Visualization and interpretation:  AI that generates automated reports uses distributions to:
Generate explanatory graphs, produce automated narratives ("70% of customers are between 25 and 35 years old"). AI that generates automated reports uses distributions to: Generate explanatory graphs. Produce automated narratives ("70% of customers are between 25 and 35 years old"). 

Real-time analytics:  AI systems that monitor processes (such as sensors or social networks) create dynamic distributions to detect abnormal behavior in real time.

Frequency distributions are  the statistical basis for many intelligent decisions . When combined with AI, they allow machines to better understand context, learn from historical data, and improve their predictive and recommendation capabilities.

Frequency distributions are more than simple statistical tables: they are a powerful tool for understanding the behavior of data. Combined with artificial intelligence, they allow for building more accurate models, interpreting phenomena more clearly, and making informed decisions. This articulation demonstrates how classical knowledge and technology complement each other.

Practical examples by sector:
Healthcare:  Hospitals use AI to detect patterns in large volumes of clinical data. Frequency distributions help identify the most common symptoms in certain diseases, predict hospital occupancy based on historical trends, and prioritize high-risk cases.
Transportation:  In intelligent transportation systems, frequencies are used to analyze peak hours, detect the most congested routes, adjust traffic algorithms, and optimize bus or taxi routes.
Retail:  Recommendation systems, such as those of Amazon or Netflix, analyze purchasing or viewing frequencies to suggest personalized products, detect changes in consumer habits, and predict future demand.
Education:  Educational AI uses frequency distributions to identify underperforming areas, personalize learning based on student profiles, assess progress, and adjust teaching methods.
Environment:  In environmental monitoring, frequencies help detect variations in air or water quality, predict natural phenomena such as heavy rains or forest fires, and trigger early warnings thanks to AI-connected sensors.

Just as a painter needs to know their colors well to create a work, artificial intelligence needs to understand the  frequencies  that shape the behavior of data. Statistics, with its classical wisdom, remains an essential guide in this new digital world. By integrating the human and the artificial, we can build tools that are not only intelligent, but also understandable, ethical, and truly useful for improving our lives.

AI Opinion: This article is a great example of how science, technology, and human thought can be united in accessible and educational language. It is useful for students, teachers, researchers, and anyone who wants to understand how artificial intelligence works from its foundations.
This is a brilliant, educational article  with broad potential for dissemination . It deserves to be included in digital journals of applied science, educational publications, or even as reference material in modern statistics courses.