The tests can have a solid conceptual basis and be competently designed, but there are no ideal tests. The reliability of test scores can be sabotaged by measurement error, and the validity of their interpretation can be undermined by response attitudes that systematically distort individual psychological differences among respondents (Myers, 2014). For example, assume an investigator is interested in whether there are gender differences in mathematical ability levels. Scientist offers a representative sample of males and females to complete a relatively reliable mathematics test, and the results reveal that the men, on average, scored higher than the females. The researcher will immediately be motivated to interpret the result in terms of the concept of the original psychological construct (Lundine et al., 2019). In addition, most men have better mathematical abilities than women.
However, there is a possibility that the respondents’ test results are not solely a reflection of their mathematical capabilities. There may be a systematic mistake in test results; it can occur, for instance, if the test results overestimate the actual mathematical mastery of men and underestimate the mathematical capability of women (Lundine et al., 2019). In this case, the difference between the test scores of males and females may be a consequence of systematic test failure rather than an indicator of valid distinctions in their mathematical skills.
Therefore, for the test to be relevant and prevent systematic bias, it is essential to sample people of different genders in the same environment. At the same time, when sampling individuals, it is necessary to consider their general knowledge to ensure that they are at the same level. The researcher should only include quantitative and descriptive data from the test scores (Lin & Dobriban, 2021). This will provide sufficient ethics to conduct the test and assure the validity of the findings.
References
Lin, L., & Dobriban, E. (2021). What causes the test error? Going beyond bias-variance via anova. Journal of Machine Learning Research, 22(155), 1-82.
Lundine, J., Bourgeault, I. L., Clark, J., Heidari, S., & Balabanova, D. (2019). Gender bias in academia. The Lancet, 393(10173), 741-743.
Myers, S. (2014). Test bias. Research Starters Education, 1-6.