A failure to isolate the controlled variables will compromise the internal validity.
Most experimental designs measures only one or two variables at a time. Any other factor, which could potentially influence the results, must be correctly controlled. Its effect upon the results must be standardized, or eliminated, exerting the same influence upon the different sample groups.
For example, if you were comparing cleaning products, the brand of cleaning product would be the only independent variable measured. The level of dirt and soiling, the type of dirt or stain, the temperature of the water and the time of the cleaning cycle are just some of the variables that must be the same between experiments. Failure to standardize even one of these controlled variables could cause a confounding variable and invalidate the results.
In many fields of science, especially biology and behavioral sciences, it is very difficult to ensure complete control, as there is a lot of scope for small variations.
Sound statistical analysis will then eliminate these fluctuations from the results. Most statistical tests have a certain error margin built in, and repetition and large sample groups will eradicate the unknown variables.
There still needs to be constant monitoring and checks, but due diligence will ensure that the experiment is as accurate as is possible.
The Value of Consistency
Controlled variables are often referred to as constants, or constant variables.
It is important to ensure that all these possible variables are isolated, because a type III error may occur if an unknown factor influences the dependent variable. This is where the null hypothesis is correctly rejected, but for the wrong reason.