In a biological experiment, there are several different variables that help a scientist discover new information. The independent variable is the aspect of the experiment that is changed or manipulated to find out an answer, while the dependent variable is the part of the experiment that is affected by the change in the independent variable. Standardized variables are those that remain the same throughout the experiment. Biological experiments are often very complex, and it's difficult to keep many variable standardized. This means that experimental results often show correlation rather than causation. That is, the independent variable may be involved in a change, but might not be the cause of the change in the dependent variable.
The standardized variables in an experiment are always the same. For example, in an experiment determining whether or not age (an independent variable) has an effect on ease of weight loss (the dependent variable), all other aspects of the experiment other than age must be the same between groups. If there is a group of 25-year-old men and a group of 45-year-old men being tested, their diets, exercise programs and stress levels must be the same. Diet, exercise and stress are standardized variables -- the variable is kept constant, or "standardized," for each group. Of course, that's not so easy to achieve, so this is an instance where you might find a connection between age and weight loss, but maybe not a causation.
Allow Broad Application
With standardized variables, experimental results can be interpreted more easily across an entire population. If an experiment studies how well a certain seed grows in heavy rainfall versus light rainfall, then factors such as light, heat, planting depth and fertilizer must be standardized. If they are standardized, then the experimenter can say the results would apply anywhere these seeds are planted. If these standardized variables change without being controlled, then there's no way to draw conclusions about the experiment. For example, if the plants all had different exposure to sunlight, then any difference in growth could be due either to the difference in rain OR the difference in sunlight.
If the other variables are standardized, then an experimenter can comfortably say that the independent variable is actually having an effect. In an experiment comparing two different types of seeds, if one group of seeds gets watered twice as much as the other group of seeds, then an experimenter has no idea if the independent variable (the type of seed) affected the results, or if it was the difference in the amount of water the seeds received that effected the change. By standardizing the variable of water -- that is, keeping it the same -- the experiment can show that the independent variable is related to the difference in growth of the seeds.
In an experiment determining if a new drug lowers cholesterol levels more than a placebo or more than another drug, the independent variable is the type of drug administered. The dependent variable is the level of cholesterol, and the standardized variables are the age of the subjects, the relative health of the subjects, the additives or fillers in the drugs/placebo, the frequency of the drug administration and the frequency with which the cholesterol levels are checked. In practice, it's very difficult to control all these other variables, so there is usually just a partial standardization for a complex study like this. This means any change found may be connected to the type of drug, but could also be due to other factors.