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How to calculate float type precision and does it make sense?

I have a problem understanding the precision of float type.
The msdn writes that precision from 6 to 9 digits. But I note that precision depends from on the size of the number:
float smallNumber = 1.0000001f;
Console.WriteLine(smallNumber); // 1.0000001
bigNumber = 100000001f;
Console.WriteLine(bigNumber); // 100000000
The smallNumber is more precise than big, I understand IEEE754, but I don't understand how MSDN calculate precision, and does it make sense?
Also, you can play with the representation of numbers in float format here. Please write 100000000 value in "You entered" input and click "+1" on the right. Then change the input's value to 1, and click "+1" again. You may see the difference in precision.

The MSDN documentation is nonsensical and wrong.
Bad concept. Binary-floating-point format does not have any precision in decimal digits because it has no decimal digits at all. It represents numbers with a sign, a fixed number of binary digits (bits), and an exponent for a power of two.
Wrong on the high end. The floating-point format represents many numbers exactly, with infinite precision. For example, “3” is represented exactly. You can write it in decimal arbitrarily far, 3.0000000000…, and all of the decimal digits will be correct. Another example is 1.40129846432481707092372958328991613128026194187651577175706828388979108268586060148663818836212158203125•10−45. This number has 105 significant digits in decimal, but the float format represents it exactly (it is 2−149).
Wrong on the low end. When “999999.97” is converted from decimal to float, the result is 1,000,000. So not even one decimal digit is correct.
Not a measure of accuracy. Because the float significand has 24 bits, the resolution of its lowest bit is about 223 times finer than the resolution of its highest bit. This is about 6.9 digits in the sense that log10223 is about 6.9. But that just tells us the resolution—the coarseness—of the representation. When we convert a number to the float format, we get a result that differs from the number by at most ½ of this resolution, because we round to the nearest representable value. So a conversion to float has a relative error of at most 1 part in 224, which corresponds to about 7.2 digits in the above sense. If we are using digits to measure resolution, then we say the resolution is about 7.2 digits, not that it is 6-9 digits.
Where do these numbers came from?
So, if “~6-9 digits” is not a correct concept, does not come from actual bounds on the digits, and does not measure accuracy, where does it come from? We cannot be sure, but 6 and 9 do appear in two descriptions of the float format.
6 is the largest number x for which this is guaranteed:
If any decimal numeral with at most x significant digits is within the normal exponent bounds of the float format and is converted to the nearest value represented in the format, then, when the result is converted to the nearest decimal numeral with at most x significant digits, the result of that conversion equals the original number.
So it is reasonable to say float can preserve at least six decimal digits. However, as we will see, there is no bound involving nine digits.
9 is the smallest number x that guarantees this:
If any finite float number is converted to the nearest decimal numeral with x digits, then, when the result is converted to the nearest value representable in float, the result of that conversion equals the original number.
As an analogy, if float is a container, then the largest “decimal container” guaranteed to fit inside it is six digits, and the smallest “decimal container” guaranteed to hold it is nine digits. 6 and 9 are akin to interior and exterior measurements of the float container.
Suppose you had a block 7.2 units long, and you were looking at its placement on a line of bricks each 1 unit long. If you put the start of the block at the start of a brick, it will extend 7.2 bricks. However, if somebody else chooses where it starts, they might start it in the middle of a brick. Then it would cover part of that brick, all of the next 6 bricks, and and part of the last brick (e.g., .5 + 6 + .7 = 7.2). So a 7.2-unit block is only guaranteed to cover 6 bricks. Conversely, 8 bricks can cover the 7.2-unit block if you choose where they are placed. But if somebody else chooses where they start, the first might cover just .1 units of the block. Then you need 7 more and another fraction, so 9 bricks are needed.
The reason this analogy holds is that powers of two and powers of 10 are irregularly spaced relative to each other. 210 (1024) is near 103 (1000). 10 is the exponent used in the float format for numbers from 1024 (inclusive) to 2048 (exclusive). So this interval from 1024 to 2048 is like a block that has been placed just after the 100-1000 ends and the 1000-10,000 block starts.
But note that this property involving 9 digits is the exterior measurement—it is not a capability that float can perform or a service that it can provide. It is something that float needs (if it is to be held in a decimal format), not something it provides. So it is not a bound on how many digits a float can store.
Further Reading
For better understanding of floating-point arithmetic, consider studying the IEEE-754 Standard for Floating-Point Arithmetic or a good textbook like Handbook of Floating-Point Arithmetic by Jean-Michel Muller et al.

Yes number of digits before rounding errors is a measure of precision but you can not asses precision from just 2 numbers because you might be just closer or further from the rounding threshold.
To better understand the situation then you need to see how floats are represented.
The IEEE754 32bit floats are stored as:
bool(1bit sign) * integer(24bit mantisa) << integer(8bit exponent)
Yes mantissa is 24 bit instead of 23 as it's MSB is implicitly set to 1.
As you can see there are only integers and bitshift. So if you are representing natural number up to 2^24 you are without rounding completely. Fro bigger numbers binary zero padding occurs from the right that causes the difference.
In case of digits after decimal points the zero padding occurs from the left. But there is another problem as in binary you can not store some decadic numbers exactly. For example:
0.3 dec = 0.100110011001100110011001100110011001100... bin
0.25 dec = 0.01 bin
As you can see the sequence of 0.3 dec in binary is infinite (like we can not write 1/3 in decadic) hence if crop it to only 24 bits you lose the rest and the number is not what you want anymore.
If you compare 0.3 and 0.125 the 0.125 is exact and 0.3 is not but 0.125 is much smaller than 0.3. So your measure is not correct unless explored more very close values that will cover the rounding steps and computing the max difference from such set. For example you could compare
1.0000001f
1.0000002f
1.0000003f
1.0000004f
1.0000005f
1.0000006f
1.0000007f
1.0000008f
1.0000009f
and remember the max difference of fabs(x-round(x)) and than do the same for
100000001
100000002
100000003
100000004
100000005
100000006
100000007
100000008
100000009
And then compare the two differences.
On top of all this you are missing one very important thing. And that is the errors while converting from text to binary and back which are usually even bigger. First of all try to print your numbers without rounding (for example force to print 20 decimal digits after decimal point).
Also the numbers are stored in binary base so in order to print them you need to convert to decadic base which involves multiplication and division by 10. The more bits are missing (zero pad) from the number the bigger the print errors are. To be as precise as you can a trick is used and that is to print the number in hex (no rounding errors) and then convert the hex string itself to decadic base on integer math. That is much more accurate then naive floating point prints. for more info see related QAs:
my best attempt to print 32 bit floats with least rounding errors (integer math only)
How do libraries/programming languages convert floats to strings
How do I convert a very long binary number to decimal?
Now to get back to number of "precise" digits represented by float. For integer part of number is that easy:
dec_digits = floor(log10(2^24)) = floor(7.22) = 7
However for digits after decimal point is this not as precise (for first few decadic digits) as there are a lot rounding going on. For more info see:
How do you print the EXACT value of a floating point number?

I think what they mean in their documentation is that depending on the number that the precision ranges from 6 to 9 decimal places. Go by the standard that is explained on the page you linked, sometimes Microsoft are a bit lazy when it comes to documentation, like the rest of us.
The problem with floating point is that it is inaccurate. If you put the number 1.05 into the site in your link you will notice that it cannot be accurately stored in floating point. It's actually stored as 1.0499999523162841796875. It's stored this way to do calculations faster. It's not great for money, e.g. what if your item is priced at $1.05 and you sell a billion of them.

The smallNumber is more precise than big
Incorrect compare. The other number has more significant digits.
1.0000001f is attempting N digits of decimal precision.
100000001f attempts N+1.
I have a problem understanding the precision of float type.
To best understand float precision, think binary. Use "%a" for printing with a C99 or later compiler.
float is stored base 2. The significand is a Dyadic rational, some integer/power-of-2.
float commonly has 24 bits of binary precision. (23-bit explicitly encoded, 1 implied)
Between [1.0 ... 2.0), there are 223 different float values.
Between [2.0 ... 4.0), there are 223 different float values.
Between [4.0 ... 8.0), there are 223 different float values.
...
The possible values of a float are not distributed uniformly among powers-of-10. The grouping of float values to power-of-10 (decimal precision) results in the wobbling 6 to 9 decimal digits of precision.
How to calculate float type precision?
To find the difference between subsequent float values, since C99, use nextafterf()
Illustrative code:
#include<math.h>
#include<stdio.h>
void foooo(float b) {
float a = nextafterf(b, 0);
float c = nextafterf(b, b * 2.0f);
printf("%-15a %.9e\n", a, a);
printf("%-15a %.9e\n", b, b);
printf("%-15a %.9e\n", c, c);
printf("Local decimal precision %.2f digits\n", 1.0 - log10((c - b) / b));
}
int main(void) {
foooo(1.0000001f);
foooo(100000001.0f);
return 0;
}
Output
0x1p+0 1.000000000e+00
0x1.000002p+0 1.000000119e+00
0x1.000004p+0 1.000000238e+00
Local decimal precision 7.92 digits
0x1.7d783ep+26 9.999999200e+07
0x1.7d784p+26 1.000000000e+08
0x1.7d7842p+26 1.000000080e+08
Local decimal precision 8.10 digits

c# print float values with more precision

I want to print floats with greater precision than is the default.
For example when I print the value of PI from a float i get 6 decimals. But if I copy the same value from the float into a double and print it i get 14 decimals.
Why do I get more precision when printing the same value but as a double?
How can I get Console.WriteLine() to output more decimals when printing floats without needing to copy it into a double first?
I also tried the 0.0000000000 but it did not write with more precision, it just added more zeroes. :-/
My test code:
float x = (float)Math.PI;
double xx = x;
Console.WriteLine(x);
Console.WriteLine(xx);
Console.WriteLine($"{x,18:0.0000000000}"); // Try to force more precision
Console.WriteLine($"{xx,18:0.0000000000}");
Output:
3,141593
3,14159274101257
3,1415930000 <-- The program just added more zeroes :-(
3,1415927410
I also tried to enter PI at https://www.h-schmidt.net/FloatConverter/IEEE754.html
The binary float representation of PI is 0b01000000010010010000111111011011
So the value is: 2 * 0b1.10010010000111111011011 = 2 * 1.57079637050628662109375 = 3.1415927410125732421875
So there are more decimals to output. How would I get C# to output this whole value?

There is no more precision in a float. When you convert it to a double, the accuracy is worse, even though the precision (number of digits) increased - basically, all those extra digits you see in the double print are conversion artifacts - they are wrong, just the best representation of that given float number you can get in a double. This has everything to do with how binary floating point numbers work.
Let's look at the binary representation of the float:
01000000010010010000111111011100
The first bit is sign, the next eight are the exponent, and the rest is the mantissa. What do we get when we cast it to a double? The exponent stays the same, but the mantissa is filled in with zeroes. But that actually changes the number - you get 3.1415929794311523 (rounded, as always with binary floats) instead of the correct 3.14159265358979 double value of pi. You get the illusion of greater precision, but it's only an illusion - the number is no more accurate than before, you just replaced zeroes with noise.
There's no 1:1 mapping between floats and doubles. The same float value can be represented by many different double values, and the decimal representation of the number can change accordingly. Every cast of float to double should be followed by a rounding operation if you care about decimal precision. Consider this code instead:
double.Parse(((float)Math.PI).ToString())
Instead of casting the float to a double, I first changed it to a decimal representation (in a string), and created the double from that. Now instead of having a "truncated" double, you have a proper double that doesn't lie about extra precision; when you print it out, you get 3.1415930000. Still rounded, of course, since it's still a binary->decimal conversion, but no longer pretending to have more precision than is actually there - the rounding happens at a later digit than in the float version, the zeroes are really zeroes, except for the last one (which is only approximately zero).
If you want real decimal precision, you might want to use a decimal type (e.g. int or decimal). float and double are both binary numbers, and only have binary precision. A finite decimal number isn't necessarily finite in binary, just like a finite trinary number isn't necessarily finite in decimal (e.g. 1/3 doesn't have a finite decimal representation).

A float within c# has a precision of 7 digits and no more. That means 1 digit before the decimal and 6 after.
If you do have any more digits in your output, they might be entirely wrong.
If you do need more digits, you have to use either double which has 15-16 digits precision or decimal which has 28-29 digits.
See MSDN for reference.
You can easily verify that as the digits of PI are a little different from your output. The correct first 40 digits are: 3.14159 26535 89793 23846 26433 83279 50288 4197

To print the value with 6 places after the decimal.
Console.WriteLine("{0:N6}", (double)2/3);
Output :
0.666667

Is the "decimal" type in C# binary-coded-decimal?

I want to handle prices in decimal currency (EUR) using C# without worrying about rounding errors and without rolling my own way of doing decimal arithmetic. The C# reference for the decimal type says
Compared to floating-point types, the decimal type has more precision and a smaller range, which makes it appropriate for financial and monetary calculations.
What?
I don't give a damn about precision, I only want about seven sig-figs. But I want to know that 5.31 EUR is an exact value. A, binary-coded-decimal type would be ideal. So my question is whether the C# decimal is that type.

No, it's not BCD (where each digit is individually encoded in a specific number of bits) - but you don't want it to be. (And I certainly never claimed it was.)
decimal is a floating point type in that it has a significand and an exponent, both integers - it's just that unlike float and double, the "point" that gets shifted by the exponent is a decimal point rather than a binary point. It's unfortunate that MSDN says "compared with floating point types" when it really means "compared with binary floating point types".
The decimal documentation does actually make it reasonably clear though:
A decimal number is a floating-point value that consists of a sign, a numeric value where each digit in the value ranges from 0 to 9, and a scaling factor that indicates the position of a floating decimal point that separates the integral and fractional parts of the numeric value.
The binary representation of a Decimal value consists of a 1-bit sign, a 96-bit integer number, and a scaling factor used to divide the 96-bit integer and specify what portion of it is a decimal fraction. The scaling factor is implicitly the number 10, raised to an exponent ranging from 0 to 28. Therefore, the binary representation of a Decimal value the form, ((-296 to 296) / 10(0 to 28)), where -(296-1) is equal to MinValue, and 296-1 is equal to MaxValue. For more information about the binary representation of Decimal values and an example, see the Decimal(Int32[]) constructor and the GetBits method.

float.TryParse() rounded the value in C#

while trying to parse variable to float with following parameter float.TryParse(value, NumberStyles.Float, CultureInfo.InvariantCulture, out fValue),
the value=6666.77777 is rounded of to 6666.778.
can anyone help, i don't want my value to be rounded.

float only has around 6 significant digits. Note that digits before the decimal point count too. double has higher precision in that regard (around 16):
PS> [float]::Parse('6666,77777')
6666.778
PS> [double]::Parse('6666,77777')
6666.77777
But as others noted, this is just an approximate value because it cannot be represented exactly in base 2. If you need decimal exactness (e.g. for money values) then use a decimal. For most other things binary floating point (i.e. float and double) should be fine.

Why don't you use Double.TryParse or Decimal.TryParse to support higher precision:
float: Approximately ±1.5 x 10-45 to ±3.4 x 1038 with 7 significant figures
double: Approximately ±5.0 x 10-324 to ±1.7 x 10308 with 15 or 16 significant figures
decimal: Approximately ±1.0 x 10-28 to ±7.9 x 1028 with 28 or 29 significant figures
Try this piece of code snippet instead:
double fValue;
double.TryParse("6666.77777", NumberStyles.Double, CultureInfo.InvariantCulture, out fValue);
OR
decimal fValue;
decimal.TryParse("6666.77777", NumberStyles.Decimal, CultureInfo.InvariantCulture, out fValue);

That is because value 666.77777 cannot be represented in the binary form floating point numbers use - the actual number contains more binary digits than the floating point has room for. The resulting number is the closest approximation.
Rounding is used when the exact result of a floating-point operation
(or a conversion to floating-point format) would need more digits than
there are digits in the significand. IEEE 754 requires correct
rounding: that is, the rounded result is as if infinitely precise
arithmetic was used to compute the value and then rounded (although in
implementation only three extra bits are needed to ensure this). There
are several different rounding schemes (or rounding modes).
Historically, truncation was the typical approach. Since the
introduction of IEEE 754, the default method (round to nearest, ties
to even, sometimes called Banker's Rounding) is more commonly used.
You should consider using double if you need more precision, or decimal if you need even more than that, though they too suffer from precision loss at some point.

you should use decimal if you need higher precision.

What is that decimal can't do but double can and vice versa?

Can someone tell what a decimal variable cannot do but at the same time double can do?
Also what is that double cant do but decimal can?
I was having trouble with finding power of (sqroot 5) to more than 2000000
e.g. (3 + root(5) )raise to 300000 ...here what can be used while using binomial expansion ?
Can I use double / decimal ? What's the main difference?
Note : I want to preserve last 3 decimal place before decimal point in the answer to the 100% accuracy.

In brief:
Decimal is a decimal floating point type, so it can represent exact decimal values, e.g. 0.1. It has a fairly high precision, but a relatively limited range. It's implemented in software, so is relatively slow.
Single/Double are binary floating point types, so they can only represent exactly numbers which can be represented exactly in binary - which doesn't include the decimal value 0.1, for example. They have relatively low precision, but a large range. It's usually implemented in hardware, so is very fast.
Additionally float/double have representations for positive and negative infinity, and "not a number" - decimal doesn't have any of this.
See my articles on binary floating point and decimal floating point for more information.

decimal.MaxValue = 79,228,162,514,264,337,593,543,950,335
double.MaxValue = 1.7976931348623157E+308
(5.24) ^ 300000 = ???
I don't think you can easily raise 5 to the power of 300000 without using a math library that is more cleaver than double....

decimal is base 10 which means he can represend 0.1 as 0.1
double is a base 2 - binary - which means he cant. ( its infinite numbers of 0,1)
double can store much bigger numbers than decimal
decimal is more accurate.,