What does a lack of random assignment in experiments potentially lead to?

A lack of random assignment in experiments can potentially lead to a reduced ability to detect true effects. This is because random assignment helps ensure that participants are allocated to different groups in a way that is not influenced by pre-existing differences between them. When random assignment is not used, it’s possible that certain biases or confounding variables might exist between the groups, which can mask the true relationship between the independent and dependent variables. As a result, any observed effects may be due to these unintended differences rather than the experimental manipulation itself. This undermines the validity of the conclusions drawn from the study, as researchers may mistakenly attribute differences in outcomes to the treatment rather than to other factors.

The other options do not accurately capture the implications of lacking random assignment. For instance, increased sample representativeness is often achieved through random sampling, not random assignment, and higher reliability in findings typically comes from well-controlled experimental conditions, which random assignment helps establish. Better control of variables often requires careful design and randomization in assignment to minimize the influence of extraneous variables, making random assignment a critical component of robust experimental design.