Number.EPSILON

2^-52, or approximately 2.2204460492503130808472633361816E-16.

Number.EPSILON is the difference between 1 and the next greater number representable in the Number format, because double precision floating point format only has 52 bits to represent the mantissa, and the lowest bit has a significance of 2^-52.

Note that the absolute accuracy of floating numbers decreases as the number gets larger, because the exponent grows while the mantissa's accuracy stays the same. Number.MIN_VALUE is the smallest representable positive number, which is much smaller than Number.EPSILON.

Because EPSILON is a static property of Number, you always use it as Number.EPSILON, rather than as a property of a number value.

Any number encoding system occupying a finite number of bits, of whatever base you choose (e.g. decimal or binary), will necessarily be unable to represent all numbers exactly, because you are trying to represent an infinite number of points on the number line using a finite amount of memory. For example, a base-10 (decimal) system cannot represent 1/3 exactly, and a base-2 (binary) system cannot represent 0.1 exactly. Thus, for example, 0.1 + 0.2 is not exactly equal to 0.3:

console.log(0.1 + 0.2); // 0.30000000000000004
console.log(0.1 + 0.2 === 0.3); // false

For this reason, it is often advised that floating point numbers should never be compared with ===. Instead, we can deem two numbers as equal if they are close enough to each other. The Number.EPSILON constant is usually a reasonable threshold for errors if the arithmetic is around the magnitude of 1, because EPSILON, in essence, specifies how accurate the number "1" is.

function equal(x, y) {
  return Math.abs(x - y) < Number.EPSILON;
}

const x = 0.2;
const y = 0.3;
const z = 0.1;
console.log(equal(x + z, y)); // true

However, Number.EPSILON is inappropriate for any arithmetic operating on a larger magnitude. If your data is on the 10³ order of magnitude, the decimal part will have a much smaller accuracy than Number.EPSILON:

function equal(x, y) {
  return Math.abs(x - y) < Number.EPSILON;
}

const x = 1000.1;
const y = 1000.2;
const z = 2000.3;
console.log(x + y); // 2000.3000000000002; error of 10^-13 instead of 10^-16
console.log(equal(x + y, z)); // false

In this case, a larger tolerance is required. As the numbers compared have a magnitude of approximately 2000, a multiplier such as 2000 * Number.EPSILON creates enough tolerance for this instance.

function equal(x, y, tolerance = Number.EPSILON) {
  return Math.abs(x - y) < tolerance;
}

const x = 1000.1;
const y = 1000.2;
const z = 2000.3;
console.log(equal(x + y, z, 2000 * Number.EPSILON)); // true

In addition to magnitude, it is important to consider the accuracy of your input. For example, if the numbers are collected from a form input and the input value can only be adjusted by steps of 0.1 (i.e. <input type="number" step="0.1">), it usually makes sense to allow a much larger tolerance, such as 0.01, since the data only has a precision of 0.1.

• Polyfill of Number.EPSILON in core-js
• Number