Why is it important to keep all variables but one constant in a scientific experiment?

Answer 1

Because changing more than one variable will change the entire experiment. Changing one variable allows you to see how things could have turned out under different circumstances.

If you keep all the variables different, you won't be able to determine what variable lead to which outcome. So just make sure you keep ONE variable different (the one you are testing) because this is crucial to your whole experiment.

Answer 2

In an experiment, you are only allowed to test for one variable at a time. This means that everything else must be the same or constant for only the one variable to be tested for. If say you were testing if plants grow better with more or less water, you need to keep everything else the same. Some constants there would be amount of sunlight and temperature of the plants. This makes it so only the variable, the amount of water the plant is given, the only thing that changes from plant to plant.

Answer 3

It's important to have a constant variable because in the experiment you need to have something that does not change. For Example: If you were to do an experiment on baking something you need to keep something the same so the experiment does not get ruined. Also a constant variable is very important for a science fair project.

Answer 4

Symbolic constants ensure constant values are used consistently, and help give meaning to those values, making our code more intuitive and easier to read.

This question really boils down to the difference between a literal constant and a symbolic constant. Consider the following:

const long double PI = {3.141592653589793};

Here we are assigning a literal constant (3.141592653589793) to a symbolic constant (PI). The value of PI is undoubtedly useful, and the symbolic constant ensures we use the value consistently. However, literal constants are often considered to be "magic numbers". Assigning them to symbolic constants certainly helps to give them meaning, however it's not always clear how the value was arrived at. In this case, we might reasonably question why the value has exactly 15 digits of precision. The reason is that 15 decimal digits is "good enough" to calculate the circumference of a circle with a radius of 12.5 billion miles to within 1.5 inches of accuracy. 12.5 billion miles is the approximate distance to the farthest man-made object: Voyager 1.

However, the problem is not so much how the value was arrived at, but why we use a literal constant when we cannot guarantee that a long double is actually capable of representing all the given digits. The representation of real numbers is entirely machine-dependent and if we can't represent 15 decimal digits of precision accurately, then we're going to end up truncating the value (narrowing). If we end up truncating the last 5 digits then it would likely be better to use the literal constant 3.1415926536 rather than 3.1415926535.

So in this particular case it would be better to calculate the constant on the actual hardware itself and thus ensure the highest possible precision across all architectures:

const long double PI {4*atan(1)};

We may well end up with more than 15 digits of precision but that's hardly a bad thing; it can only improve the precision (however slightly). But we don't sacrifice precision on systems that truncate in unpredictable ways.

This is just one example of a symbolic constant, however it's a classic example of why literal constants should be avoided unless their usage is completely transparent. If we have to question why a literal constant has a particular value, then we should use a symbolic constant instead. It costs absolutely nothing to name a constant. Literal constants such as 0, 1, true , false, yes, no, black, white and so on are usually self-explanatory, however with reversed logic where 0 indicates success and 1 indicates failure, symbolic constants help bring clarity to our code:

const int ERROR_SUCCESS {0};

const int ERROR_FAILURE {1};

Answer 5

because, that's what stays the same in an experiment and you ask yourself what group did NOT get the variable?

Answer 6

To find out if your scientific prediction was correct you have to keep experimenting and then test out your data.