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Apprendre Challenge: Floating Point Precision | Primitive Data Types
Java Data Types

bookChallenge: Floating Point Precision

Understanding how floating point numbers behave in Java is crucial when you work with calculations that require precision. Unlike integers, floating point numbers such as float and double are subject to rounding errors caused by the way computers represent decimal values in binary. This can lead to surprising results, especially when performing simple arithmetic like adding 0.1 and 0.2.

To see this in action, you will write a small program that uses both float and double to add these two numbers and prints the results. Observing the output will help you recognize why floating point arithmetic can sometimes produce unexpected results and why you must be aware of these limitations when working with real-world data.

Tâche

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Write a Java program that demonstrates floating point precision issues by separating the calculation logic into its own method. Your program should:

  • Create a method named calculateSums that adds 0.1 and 0.2 using both float and double types.
  • The calculateSums method should return a data structure (such as an array) containing both sums.
  • In the main method, call calculateSums and print both results with clear labels.
  • Use the following template for your code.

Solution

solution.java

solution.java

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Section 1. Chapitre 4
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bookChallenge: Floating Point Precision

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Understanding how floating point numbers behave in Java is crucial when you work with calculations that require precision. Unlike integers, floating point numbers such as float and double are subject to rounding errors caused by the way computers represent decimal values in binary. This can lead to surprising results, especially when performing simple arithmetic like adding 0.1 and 0.2.

To see this in action, you will write a small program that uses both float and double to add these two numbers and prints the results. Observing the output will help you recognize why floating point arithmetic can sometimes produce unexpected results and why you must be aware of these limitations when working with real-world data.

Tâche

Swipe to start coding

Write a Java program that demonstrates floating point precision issues by separating the calculation logic into its own method. Your program should:

  • Create a method named calculateSums that adds 0.1 and 0.2 using both float and double types.
  • The calculateSums method should return a data structure (such as an array) containing both sums.
  • In the main method, call calculateSums and print both results with clear labels.
  • Use the following template for your code.

Solution

solution.java

solution.java

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Tout était clair ?

Comment pouvons-nous l'améliorer ?

Merci pour vos commentaires !

Section 1. Chapitre 4
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