Compare clinical significance and statistical significance.

Assignment: Inferential Statistics Discussion

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Week 6 discussion Data Results and Analysis After the data are collected, it is time to analyze the results! Discuss one of the four basic rules for understanding results in a research study. Compare clinical significance and statistical significance. Which one is more meaningful when considering applying evidence to your practice? Compare descriptive statistics and inferential statistics in research. Please give an example of each type that could be collected in a study that would be done on your nursing clinical issue you identified in previous weeks.

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Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution.[1] Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population.

Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population.

Statistical inference makes propositions about a population, using data drawn from the population with some form of sampling. Given a hypothesis about a population, for which we wish to draw inferences, statistical inference consists of (first) selecting a statistical model of the process that generates the data and (second) deducing propositions from the model.[citation needed]

Konishi & Kitagawa state, “The majority of the problems in statistical inference can be considered to be problems related to statistical modeling”.[2] Relatedly, Sir David Cox has said, “How [the] translation from subject-matter problem to statistical model is done is often the most critical part of an analysis”.[3]

The conclusion of a statistical inference is a statistical proposition.[4] Some common forms of statistical proposition are the following:

a point estimate, i.e. a particular value that best approximates some parameter of interest;
an interval estimate, e.g. a confidence interval (or set estimate), i.e. an interval constructed using a dataset drawn from a population so that, under repeated sampling of such datasets, such intervals would contain the true parameter value with the probability at the stated confidence level;
a credible interval, i.e. a set of values containing, for example, 95% of posterior belief;
rejection of a hypothesis;[note 1]
clustering or classification of data points into groups.