Examining a huge amount of data is a typical issue in any research process. However, different statistical processes and techniques play essential role to derive a meaningful conclusion from the presented enormous data. Control of type I error is highly essential for a researcher or statistician while dealing with comparisons test with more than two variables. Multiple testing statistical tests provides a structural system and minimizes the error rate by helping to derive meaningful accurate conclusions. Among the different multiple test procedures Tukey's honestly significant difference test (Tukey's HSD) is most common and popular techniques. The main objective of this study was to explore how significantly selection of confidence level or error rate can affect the rate of committing type I error while drawing conclusion. The effect of committing type I error with selection of confidence level or error rate was explored with citing suitable case study in a special education setting. The case study focuses cognitive effect of music on growth and enhancement of the motor behavior (running, jumping and sliding) of children with mild intellectual disability enrolled in the special school setting. ANOVA test was performed and the significance of selection of individual confidence level and simultaneous confidence level) in Tukey's HSD test was described.

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Child anaemia is a serious global health issue and India is one of the highest contributors among the developing nations. Researchers identify many harmful effects of anaemia, which include psychomotor retardation, which in turn decreases the learning ability and causes low intelligence among pre-school children. The effects also include behavioural delays, low immunity, and susceptibility to frequent infections, increased mortality, and disability. The present study aims to predict anaemia among children in North-East India by applying Machine Learning (ML) algorithms to latest available National Family Health Survey (NFHS)-4 data. Out of the total 29,312 eligible children (6–59 months) in North-East India, a total of 21,000 children with demographic variables without any missing observations, wherein 10,460 are anaemic, is considered for this study. Machine learning (ML) algorithms have been applied through 3 different types of penalized regression methods—ridge, least absolute shrinkage and selection operator, and elastic net for predicting anaemia. A systematic assessment of algorithms is performed in terms of accuracy, sensitivity, specificity, F1-Score, and Cohen’s -Statistics. Having achieved the receiver operating characteristic value of over 70% in training and accuracy of above 64% while testing, it can be safely asserted that factors like mother’s anaemic status, age of the child, social status, mother’s age, mother’s education, religion are important in identifying the child as anaemic.

Collection of data and to check its suitability is the first step in any statistical data analysis. In such analyses, the presence of outliers appears as an unavoidable important problem. Outliers are unexpected random values in dataset, and they can alter the statistical conclusion and also affect their assumptions. Thus, in order to manage the data properly, outliers must be defined and treated. So all statisticians have to confront the analysis and forced to take a decision. There is only being one of the two extreme choices left for the researcher or statistician during the analysis of outliers. First, either to reject the outlier with the risk of loss of genuine information and the second one is to include them with the risk of error in drawing conclusion. The study therefore summarize  the various potential causes of extreme scores in a data set (e.g., data recording or entry errors, sampling errors, and legitimate sampling), how to detect them, and whether they should be removed or not. Another objective of this study was to explore how significantly a small proportion of outliers can affect even simple analyses. The study was explored with citing suitable examples including outlier value and also excluding the outlier data. The examples show a strong beneficial effect of repetition of the study based on extreme of scores. One way ANOVA test was performed and the significance of extreme outlier was described.

This paper focuses on examining the influence of parental literacy status in the implementation of Dance Movement Therapy (DMT) for the development of motor skills in children with intellectual disabilities in India. The study aims to explore the potential impact of parental literacy on engagement, participation, and outcomes of DMT interventions, ultimately informing strategies to optimize therapy effectiveness. The research incorporates the use of the Behavioral Assessment Scales for Indian Children with Mental Retardation (Basic-MR, Part A) to assess the impact of parental literacy on the outcomes of DMT interventions. Literacy status of parents, particularly in low- and middle-income countries like India, can significantly affect various aspects of a child's development, including their access to education, healthcare, and therapies. However, there is limited research exploring the specific role of parental literacy in the context of DMT interventions for children with intellectual disabilities in India. This study adopts a mixed-methods research design, combining quantitative measurements and qualitative insights. A sample of children with intellectual disabilities, aged between 7 and 15 years, will be recruited from special education schools from Odisha, India.  The DMT intervention will be carried out over a designated period, with trained dance movement therapists tailoring the sessions to meet the individual needs and abilities of the children. The impact of the intervention will be evaluated using the Behavioral Assessment Scales for Indian Children with Mental Retardation (Basic-MR, Part A), which provides a comprehensive assessment of motor skills including locomotor and object control skills. Quantitative data, including pre- and post-intervention assessments, will be analyzed to determine the influence of parental literacy on motor skill development outcomes. This study aims to enhance our understanding of how parental literacy status influences the implementation of DMT and motor skill development in children with intellectual disabilities in India. By recognizing the impact of parental literacy and addressing potential challenges, the study aims to optimize the effectiveness of DMT interventions for children with intellectual disabilities. The findings will have practical implications for therapists, educators, policymakers, and other stakeholders involved in culturally sensitive intervention programs, promoting comprehensive motor skill development in children with intellectual disabilities in India.