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The Classical Test Theory, often known as CTT, is a statistical theory that is used in the process of creating tests as well as analyzing the results of such tests. The primary objective of the CTT is to validate and verify the accuracy of the results of standardized tests. The CTT is predicated on a number of assumptions, the most important of which are that the test is a measurement of the latent trait of interest, that the items on the test are independent of one another, and that the items on the test all have the same level of difficulty. The fact that the CTT isn't too complicated both to comprehend and use is one of its primary selling points. In addition to this, CTT may be used to determine the reliability as well as the validity of a test. CTT, on the other hand, has a number of drawbacks, including the fact that it is dependent on a number of assumptions that may not be practical and the fact that it does not offer information on the difficulty of the item or the test taker's aptitude (Cohen, Schneider, & Tobin 2022).

The statistical theory known as Item Response Theory, or IRT, is used in the process of creating and analyzing tests. The primary objective of item-response theory (IRT) is to provide an assessment of both the difficulty of the item and the ability of the test taker. The item response theory (IRT) is founded on a number of assumptions, the most important of which are that the test is a measure of the latent trait of interest, that the items on the test are conditionally independent of each other, and that the items on the test are unidimensional. The fact that IRT may be used to evaluate both the difficulty of the question and the skill of the person taking the exam is one of its primary benefits. In addition to this, IRT may be used in the process of evaluating the psychometric qualities of a test. IRT, on the other hand, has a number of drawbacks, the most notable of which are the fact that it is dependent on a number of assumptions that may not be practical and the fact that it is more difficult to grasp and use than CTT (Cohen, Schneider, & Tobin 2022).

The CTT and the IRT are both statistical theories that may be used in the process of creating andanalyzing tests. The two competing ideas each have a lot of positive and negative aspects. Although the CTT is straightforward in both its explanation and its application, it is founded on a number of presumptions that may or may not be grounded in reality. IRT is more difficult to comprehend and use, but it may be used to make estimates about the difficulty of the items and the ability of the person taking the exam. Both CTT and IRT have their positive and negative aspects, and as a result, it is up to the person who is developing the test to choose which theory is more suited to meet their needs. The Wechsler Adult Intelligence Scale and the Stanford-Binet Intelligence Scale are two instances of classical test theory that have been put into practice. The Rasch model and the two-parameter logistic model are two instances of the Item Response Theory (Cohen, Schneider & Tobin 2022).

Technological advances have become indispensable with test and assessments. Technology assist with response data and processing the data. "Digital assessments facilitate the creation of new types of stimuli and response formats that were out of reach for assessments using paper; for instance, interactive simulations may include multimedia elements, as well as virtual or augmented realities" (Goldhammer, Scherer, & Greiff 2020). The future of technological based assessments can possibly extend the traditional assessment paradigm and allow for further enhancements. There are also implications for ethnic and cultural aspects in testing and allow for better assessments world wide.