Provide a substantive contribution that advances the discussion in a meaningful way by identifying strengths of the posting, challenging assumptions, and asking clarifying questions.
A null hypothesis is a statistical test which assumes there is no significant statistical differences exist between populations and if there is a difference, the difference is due to error (Warren, 2013). In other words, a null hypothesis is an accepted fact which has to be invalidated or nullifying the data.
An alternative hypothesis is the data or information which invalidates or nullifies the null hypothesis by trying to determine the cause of difference which may exist between populations (Warren, 2013). In other words, the alternative hypothesis gives the reason the null hypothesis is invalid.
My areas of specialization is applied behavior analysis. A study by Hilton, et. al (2016) studied sensory responsiveness as well as the relationship between sensory responsiveness and social severity in siblings of children with autism spectrum disorder (ASD) who do not have autism. To thoroughly understand the genetics and core issues related to ASD, sensory characteristics of siblings without autism is essential (Hilton et. al, 2016).
Significant differences were found between participants with ASD and controls as well as between participants with ASD and siblings without autism for all sensory quadrants and domains. There were no significant differences between controls and siblings without autism. Social responsiveness and most sensory profile categories scores were significantly correlated. It was found siblings without ADS do not display atypical sensory trails as an endophenotype. However, this study is unable to uphold the null hypothesis as studies with larger numbers of siblings without autism are needed (Hilton et. al, 2016).
Hilton, C. L., Babb-keeble, A., Westover, E. E., Zhang, Y., Adams, C., Collins, D. M., Constantino, J. N. (2016). Sensory responsiveness in siblings of children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 46(12), 3778-3787. doi:http://dx.doi.org.library.capella.edu/10.1007/s10803-016-2918-y
Warner, R. M. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: Sage Publications.
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