The word “ heterogeneity” is used to describe a mixture of different items, in contrast with “homogeneity,” which suggests that something has a uniform composition. There are a number of settings in which these terms can be used, in everything from statistical analysis of data to discussions about regional ecology.
In some cases, heterogeneity is a desirable trait. For example, when evaluating a natural environment, a diverse mix of species, objects, and types of organisms is a good thing, because it suggests that the environment is healthy, capable of supporting many types of organisms. Likewise, in evaluations of the genetics of a population, heterogeneity of genetic material suggests robustness and diversity, while homogeneity in a population can be a sign that the population is vulnerable to problems.
In genetics, heterogeneity suggests that genetic material is being exchanged at a brisk rate between diverse individuals. This indicates that negative traits are more likely to drop out of a population, while positive traits can be brought forward. By contrast, homogeneous genetic populations tend to amplify negative traits, and they are extremely vulnerable to disease. For example, if all of the plants in a field carry a gene which can cause the plant to get sick if it is exposed to a particular fungus and the fungus enters the field, all of the plants will sicken. By contrast, if 25% of the plants carry the gene, those will die, but the remainder will stay healthy.
When any substance is evaluated scientifically, one of the qualities assessed is heterogeneity, whether a technician is analyzing a blood sample or trying to determine the constituent components of an unknown compound. In addition to reflecting a mixture of components, heterogeneity can also appear in the form of a mixture of structures. Milk, for example, is naturally heterogeneous, but it is often processed so that it becomes homogenized, ensuring that the components of the milk will not separate out before people get a chance to drink it.
Heterogeneity can also be a desirable trait in a statistical sample. Scientists generally prefer to see large, diverse samples when it comes to statistics, rather than smaller, limited samples. If a sample is largely homogeneous in composition, the results can be difficult to apply to other populations or to the world in general, while a heterogeneous sample is viewed to have more statistical validity. There are a number of ways to evaluate the composition of a sample to find out whether it is varied enough to satisfy standards of validity.