The basic types in C are just there to standardize a single unit of memory (a block of bits) that can be used to store a value. We all know that a computer uses bits as its basic unit of information, however a bit is too small to be useful in and of itself, by defining different types we define different blocks of bits and how to interpret them. When we say "int a", the compiler knows that we are dealing with a basic, signed binary number, and knows the amount of memory it needs to set aside to store that number.

The biggest issue is that the exact size definitions are system dependent and not defined in the specifications.

1. A float and double are both implementations of floating point values in C. floating point numbers are a way to implement decimal and fractional values, of any magnitude in binary and also streamline their arithmetic. It is essentially a binary version of scientific notation. Imagine writing the number 3.14159 In decimal, The mantissa here would be 314159 and the exponent would be -5. You could define a simple function that takes the two numbers (stored in binary) and prints out their combined decimal value, your program could therefore store all decimal numbers as two different numbers in binary, and use its own functions to add them together, print them etc. However, C streamlines this process by giving you a simple container that does all that work for you behind the scene. A floating point standard describes how you would encode the mantissa and exponent into a single binary number. In C, the only difference between a float and a double is the amount of memory set aside to store your number, and thus the greater range and precision of the numbers that you can store. The benefit of this is that you can also describe large integer numbers, for instance, you would need 32 bits to write the number "4 billion" explicitly in binary, but you only need 8 to do the same in floating point (4 x 10^9). However, you would not be able to write the number 4,123,456,789 as an 8 bit floating point or even a 32 bit floating point number (as you still need to encode the exponent), but it will fit perfectly well in a 32 bit int.

2. A char and int are just basic binary numbers, their only difference being in length (potentially). A character is defined as the smallest unit of data necessary to hold a single text character for that architecture, this is a bit abstractly defined, but for most computer today running x86 platforms, a char is defined as 8 bits (although, on unicode systems it could be 16 bits). An integer is another basic binary number, but it is required to be at least 16 bits long (typically it is either 32 or 64 on modern systems). So a 8 bit char is capable of encoding 256 unique characters, or a number between 0-255 (or a number between -127 and 128) the difference is only in how you interpret the collection of bits. An 32 bit int can store a number between 0 and slightly over 4 billion, or you could half that range and use it to represent a number between (roughly) -2 billion and 2 billion

On some systems, the size of an int may be the same as the size of float, from the memory point of view they are just collections of bits, however the processor will interpret the values differently, and will actually use different circuits to add two numbers together if they are floats vs if they ints.

Huge difference.

As the name implies, a double has 2x the precision of float^[1]. In general a double has 15 decimal digits of precision, while float has 7.

Here's how the number of digits are calculated:

double has 52 mantissa bits + 1 hidden bit: log(2⁵³)÷log(10) = 15.95 digits

float has 23 mantissa bits + 1 hidden bit: log(2²⁴)÷log(10) = 7.22 digits

This precision loss could lead to greater truncation errors being accumulated when repeated calculations are done, e.g.

float a = 1.f / 81;
float b = 0;
for (int i = 0; i < 729; ++ i)
    b += a;
printf("%.7g\n", b); // prints 9.000023

while

double a = 1.0 / 81;
double b = 0;
for (int i = 0; i < 729; ++ i)
    b += a;
printf("%.15g\n", b); // prints 8.99999999999996

Also, the maximum value of float is about 3e38, but double is about 1.7e308, so using float can hit "infinity" (i.e. a special floating-point number) much more easily than double for something simple, e.g. computing the factorial of 60.

During testing, maybe a few test cases contain these huge numbers, which may cause your programs to fail if you use floats.

Of course, sometimes, even double isn't accurate enough, hence we sometimes have long double^[1] (the above example gives 9.000000000000000066 on Mac), but all floating point types suffer from round-off errors, so if precision is very important (e.g. money processing) you should use int or a fraction class.

Furthermore, don't use += to sum lots of floating point numbers, as the errors accumulate quickly. If you're using Python, use fsum. Otherwise, try to implement the Kahan summation algorithm.

^{[1]: The C and C++ standards do not specify the representation of float, double and long double. It is possible that all three are implemented as IEEE double-precision. Nevertheless, for most architectures (gcc, MSVC; x86, x64, ARM) float is indeed a IEEE single-precision floating point number (binary32), and double is a IEEE double-precision floating point number (binary64).}

float (the C# alias for System.Single) and double (the C# alias for System.Double) are floating binary point types. float is 32-bit; double is 64-bit. In other words, they represent a number like this:

10001.10010110011

The binary number and the location of the binary point are both encoded within the value.

decimal (the C# alias for System.Decimal) is a floating decimal point type. In other words, they represent a number like this:

12345.65789

Again, the number and the location of the decimal point are both encoded within the value – that's what makes decimal still a floating point type instead of a fixed point type.

The important thing to note is that humans are used to representing non-integers in a decimal form, and expect exact results in decimal representations; not all decimal numbers are exactly representable in binary floating point – 0.1, for example – so if you use a binary floating point value you'll actually get an approximation to 0.1. You'll still get approximations when using a floating decimal point as well – the result of dividing 1 by 3 can't be exactly represented, for example.

As for what to use when:

• For values which are "naturally exact decimals" it's good to use decimal. This is usually suitable for any concepts invented by humans: financial values are the most obvious example, but there are others too. Consider the score given to divers or ice skaters, for example.

• For values which are more artefacts of nature which can't really be measured exactly anyway, float/double are more appropriate. For example, scientific data would usually be represented in this form. Here, the original values won't be "decimally accurate" to start with, so it's not important for the expected results to maintain the "decimal accuracy". Floating binary point types are much faster to work with than decimals.

• float stores floating-point values, that is, values that have potential decimal places
• int only stores integral values, that is, whole numbers

So while both are 32 bits wide, their use (and representation) is quite different. You cannot store 3.141 in an integer, but you can in a float.

Dissecting them both a little further:

In an integer, all bits except the leftmost one are used to store the number value. This is (in Java and many computers too) done in the so-called two's complement, which support negatives values. Two's complement uses the leftmost bit to store the positive (0) or negative sign (1). This basically means that you can represent the values of −2³¹ to 2³¹ − 1.

In a float, those 32 bits are divided between three distinct parts: The sign bit, the exponent and the mantissa. They are laid out as follows:

S EEEEEEEE MMMMMMMMMMMMMMMMMMMMMMM

There is a single bit that determines whether the number is negative or non-negative (zero is neither positive nor negative, but has the sign bit set to zero). Then there are eight bits of an exponent and 23 bits of mantissa. To get a useful number from that, (roughly) the following calculation is performed:

M × 2^E

(There is more to it, but this should suffice for the purpose of this discussion)

The mantissa is in essence not much more than a 24-bit integer number. This gets multiplied by 2 to the power of the exponent part, which, roughly, is a number between −128 and 127.

Therefore you can accurately represent all numbers that would fit in a 24-bit integer but the numeric range is also much greater as larger exponents allow for larger values. For example, the maximum value for a float is around 3.4 × 10³⁸ whereas int only allows values up to 2.1 × 10⁹.

But that also means, since 32 bits only have 4.2 × 10⁹ different states (which are all used to represent the values int can store), that at the larger end of float's numeric range the numbers are spaced wider apart (since there cannot be more unique float numbers than there are unique int numbers). You cannot represent some numbers exactly, then. For example, the number 2 × 10¹² has a representation in float of 1,999,999,991,808. That might be close to 2,000,000,000,000 but it's not exact. Likewise, adding 1 to that number does not change it because 1 is too small to make a difference in the larger scales float is using there.

Similarly, you can also represent very small numbers (between 0 and 1) in a float but regardless of whether the numbers are very large or very small, float only has a precision of around 6 or 7 decimal digits. If you have large numbers those digits are at the start of the number (e.g. 4.51534 × 10³⁵, which is nothing more than 451534 follows by 30 zeroes – and float cannot tell anything useful about whether those 30 digits are actually zeroes or something else), for very small numbers (e.g. 3.14159 × 10⁻²⁷) they are at the far end of the number, way beyond the starting digits of 0.0000...