Experimental design is not typically used in special education data analysis because it is difficult to control for all variables that may affect the outcome of an intervention.

Descriptive statistics are used to summarize and describe data, such as the mean, median, mode, and range.

The t-test is commonly used to compare the means of two groups.

Qualitative analysis is used to understand the experiences and perspectives of individuals, such as through interviews, observations, and document analysis.

Experimental design is not a data collection method, but rather a research design used to test hypotheses.

Data analysis is used to identify areas of need, evaluate the effectiveness of interventions, and make decisions about placement and services.

Excel is not a specialized data analysis software, although it can be used for basic data analysis tasks.

Data privacy and confidentiality are important to protect the rights of individuals, ensure the accuracy of data, and maintain the integrity of research.

Personality tests are not typically used in special education assessment.

The IEP outlines the student's present levels of performance, establishes annual goals and objectives, and determines appropriate services and supports.

Counseling is not typically considered a special education intervention.

Progress monitoring is used to track student progress towards goals and objectives, identify areas where students need additional support, and make decisions about changes to the IEP.

Medical records are not typically used in special education data analysis.

Collaboration is important in special education data analysis to ensure that all stakeholders are involved in the decision-making process, share expertise and knowledge, and develop a comprehensive understanding of the student's needs.

Generalizability is not an ethical consideration in special education data analysis.