Final answer:
In statistics, error variance is caused by a variety of factors including treatment differences among participants, individual characteristics, mood variances, and measurement errors. Hence, when evaluating outcomes of a study, all these aspects can lead to variability that is unrelated to the main variables being investigated.
Step-by-step explanation:
Error variance in statistics refers to the variability in scores of a study's outcome that cannot be attributed to the variables being studied. Instead, it arises from a multitude of extraneous factors. The sources of error variance include:
- Treating individual participants differently, which introduces inconsistency in how the experiment or study is conducted across subjects.
- Differences in participants' personal characteristics such as age, education, or background could affect their responses or behaviors, contributing to variability.
- The participants' current moods can influence their performance or responses, adding to the error variance.
- Measurement error, which encompasses the inaccuracies that can arise from the instruments or methods used to collect data.
In conclusion, all of the options listed contribute to error variance in a study or experiment.