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Null Hypothesis Significance Testing
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About the lecture
In this lecture, we think about null hypothesis significance testing (NHST), focusing in particular on: (i) inferential statistics and how they can provide estimates for the representativeness of a sample compared to a population, and whether there is a significant difference between two or more datasets; (ii) the underpinnings of the null hypothesis, highlighting that it is only ever possible reject or fail to reject a hypothesis, rather than prove one to be correct; (iii) the four principles of NHST: scientific questions are posed in pairs of hypotheses (null and alternative), inferential statistics are used to assess whether a hypothesis is or is not true, the p-value (probability of that truth) is compared to a threshold level, and that a hypothesis is never accepted, only rejected; (iv) an analogy contextualising the idea of proof in a courtroom example on verdict of guilt.
About the lecturer
Dr Andrew Bell is a lecturer in cognitive neuroscience in the Institute of Psychiatry, Psychology and Neuroscience at King’s College London. Dr Bell works in the neuroimaging department and focuses his teaching on statistics and statistical techniques. Dr Bell is a senior fellow of the Higher Education Academy and has published a review of animal lesion techniques used by researchers of human neuropsychology. Dr Bell’s recent publications include ‘Preserved extrastriate visual network in a monkey with substantial, naturally occurring damage to primary visual cortex’ (2019) and ‘Viewing ambiguous social interactions increases functional connectivity between frontal and temporal nodes of the social brain’ (2021).
Cite this Lecture
APA style
Bell, A. (2022, February 15). Observed Values - Null Hypothesis Significance Testing [Video]. MASSOLIT. https://massolit.io/options/observed-calculated-values?auth=0&lesson=4973&option=8505&type=lesson
MLA style
Bell, A. "Observed Values – Null Hypothesis Significance Testing." MASSOLIT, uploaded by MASSOLIT, 15 Feb 2022, https://massolit.io/options/observed-calculated-values?auth=0&lesson=4973&option=8505&type=lesson