NURS341 Lecture Notes - Lecture 6: Central Limit Theorem, Location Test, Null Hypothesis
14 Mar 2018
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Document Summary
Inferential statistical analysis based on a set of assumptions about the population: Use central limit theorem here to address this assumption & look at your sample distribution. T-test = co(cid:373)pari(cid:374)g mea(cid:374)s 2 groups no(cid:373)i(cid:374)al or ordi(cid:374)al le(cid:448)el. Single sample - known population mean compared to sample mean. Independent sample different participants in each sample (ex: comparing units) Dependent sample (paired t-test) data of participants under 2 conditions or participants are matched so we calculate a mean difference b/w 2 values for each pair. Outcome variable measured at interval or ratio level. If you have outliers, or bimodal, or if median is a better descriptor of central tendency, reduce data and move to non-parametric. T-value has to be greater than the critical value of t and alpha = 0. 05 in order to reject the null hypothesis (significant finding) Level of significance: probability of making a type 1 error. Whether to do a one-tailed or two-tailed test.
