Cultural bias is not a type of language testing bias. It is a type of bias that occurs when a test is designed or administered in a way that favors or disfavors a particular cultural group.

All of the above can be causes of construct bias. Inadequate test design, unclear test instructions, and lack of cultural sensitivity can all lead to a test that does not accurately measure the intended construct.

All of the above can be consequences of item bias. Item bias can lead to lower scores for certain groups of test takers, increased anxiety and stress for test takers, and reduced validity and reliability of the test.

All of the above are strategies to reduce scoring bias. Using standardized scoring procedures, providing training for scorers, and conducting regular audits of scoring practices can all help to ensure that tests are scored fairly and consistently.

A science question about the life cycle of a butterfly is an example of a culturally sensitive test item because it is likely to be familiar to test takers from a variety of cultural backgrounds.

All of the above can be challenges in addressing language testing bias. Lack of awareness of bias, resistance to change, and limited resources can all make it difficult to implement changes that reduce bias in language testing.

All of the above are benefits of addressing language testing bias. Addressing language testing bias can lead to increased fairness and equity in language testing, improved validity and reliability of language tests, and reduced anxiety and stress for test takers.

All of the above are recommended practices for developing culturally sensitive language tests. Involving members of the target population in the test development process, using a variety of item formats and question types, and providing clear and concise test instructions can all help to reduce bias and ensure that the test is fair and equitable for all test takers.

All of the above are limitations of using statistical methods to detect language testing bias. Statistical methods can only detect bias if it is large enough, they cannot detect bias that is caused by subjective factors, and they can be difficult to interpret.

All of the above are promising approaches to reducing bias in language testing. Universal design for learning, computer-adaptive testing, and item response theory can all be used to develop tests that are fair and equitable for all test takers.

All of the above are key considerations in addressing language testing bias. Contextualizing the test, considering the purpose of the test, and understanding the test takers' backgrounds can all help to ensure that the test is fair and equitable for all test takers.

All of the above are examples of tests that are likely to be biased against non-native speakers of English. Complex and unfamiliar vocabulary, long essays, and time limits can all disadvantage non-native speakers who may not be as proficient in English as native speakers.

All of the above are strategies to reduce bias in scoring language tests. Using a variety of scoring methods, providing training for scorers, and conducting regular audits of scoring practices can all help to ensure that tests are scored fairly and consistently.

All of the above are challenges in developing culturally sensitive language tests. Lack of awareness of cultural differences, resistance to change, and limited resources can all make it difficult to develop tests that are fair and equitable for all test takers.

All of the above are benefits of using computer-adaptive testing to reduce language testing bias. Computer-adaptive testing can adapt to the ability level of each test taker, reduce the amount of time it takes to complete a test, and provide more accurate feedback to test takers.