Hae‐Yeong Kim

As discussed in the previous statistical notes, although many statistical methods have been proposed to test normality of data in various ways, there is no current gold standard method. The eyeball test may be useful for medium to large sized (e.g., n > 50) samples, however may not useful for small samples. The formal normality tests including Shapiro-Wilk test and Kolmogorov-Smirnov test may be used from small to medium sized samples (e.g., n < 300), but may be unreliable for large samples. Mor

Mean values obtained from different groups with different conditions are frequently compared in clinical studies. For example, two mean bond strengths between tooth surface and resin cement may be compared using the parametric Student's t test when independent groups are subjected to the comparison under the assumptions of normal distribution and equal variances (or standard deviation). In a condition of unequal variances we may apply the Welch's t test as an adaptation of the t test. As the nat

For comparison of three or more group means we apply the analysis of variance (ANOVA) method to decide if all means are equal or there is at least one mean which is different from others. If we get a significant result, we can conclude a global decision that there is difference in group means. However then we need to know what specific pairs of group means show differences and what pairs do not. The procedure is performed by post-hoc multiple comparison procedures.

Iron-responsive elements (IREs) are RNA stem-loop motifs found in genes of iron metabolism. When cells are iron-depleted, iron regulatory proteins (IRPs) bind to IREs in the transcripts of ferritin, transferrin receptor, and erythroid amino-levulinic acid synthetase. Binding of IRPs to IRE motifs near the 5' end of the transcript results in attenuation of translation while binding to IREs in the 3'-untranslated region of the transferrin receptor results in protection from endonucleolytic cleavag

In most clinical studies, p value is the final result of data analysis. A small p value is interpreted as a significant difference between the experimental group and the control group. However, reporting p value is not enough to know the actual difference. Problem of p value is that it depends on the sample size, n. Even a trivial meaningless difference can result in an extremely small p value when sample size is large. To make up this weak point, we need to report the 'effect size' as well as t

Evaluation of measurement error 2: Dahlberg's error, Bland-Altman method, and Kappa coefficient

Iron regulatory proteins (IRPs) bind to specific RNA stem-loop structures known as iron-responsive elements (IREs) which mediate the post-transcriptional regulation of many genes of iron metabolism. Most studies have focused on the role of IRP1, which has previously been shown to bind with high affinity to IREs and mediate repression of in vitro translation of ferritin mRNAs. More recently, a second IRP has been identified that is expressed in all tissues and that binds IREs (Rouault, T. A., Hai

Asking advice about sample size calculation is one of frequent requests from clinical researchers to statisticians. Sample size calculation is essential to obtain the results as the researcher expects as well as to interpret the statistical results as reasonable one. Usually insignificant results from studies with too small sample size may be subjects to suspicion about false negative, and also significant ones from those with too large sample size may be subjects to suspicion about false positi

For comparison of three or more independent groups, Kruskal-Wallis test which is comparable to one-way ANOVA is used. Also for analysis of correlated data with three or more occasions or conditions, Friedman test, comparable to repeated measures one-way ANOVA, is used as a nonparametric method.