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In statistics, Cronbach's alpha coefficient is used as an estimate of the reliability of a psychometric test. It might have calculated either for each item in the scale or an average value for all items in the scale. The reliability of the scale is accepted as good if the coefficient is found equal or greater than 0.70. On the other hand, the results should be considered whether if a low value of alpha that could be due to a low number of questions in the scale and a high value of alpha (>0.90) may suggest redundant items in the scale.

Kappa coefficient is a statistic which measures inter-rater agreement for categorical items. It is generally thought to be a more robust measure than simple percent agreement calculation, since ? takes into account the agreement occurring by chance. Cohens kappa measures agreement between two raters only but Fleiss kappa is used when there are more than two raters. ? may have a value between -1 and +1. A value of kappa equal to +1 implies perfect agreement between the two raters, while that of -1 implies perfect disagreement. If kappa assumes the value 0, then this implies that there is no relationship between the ratings of the two observers, and any agreement or disagreement is due to chance alone. [PBS 2015; 5(3.000): 142-4]

There are two major errors in medical research. Random errors, which we named as error by chance, and systematic errors, which we called as bias, affect the internal and external validity of researches. The aim of this technical note is to give some information about different bias causes under two major bias headings as \"selection and information bias\". It is possible to decrease the probability of random error by increasing the sample size. But systematic error is not prevented by increasing sample size unless the representativeness is not provided. Also information bias, which will occur during planning and conducting of research, will make us away from the truth. Therefore it is very important to consider probable bias causes during planning and conducting of any research from beginning to the end.

Purpose: To determine the effect of a capsular tension ring (CTR) implantation in preventing posterior capsular opacification (PCO) after cataract surgery in patients with high myopia. Materials and Methods: In this prospective single-surgeon standardized-surgical-procedure fellow-eye comparison trial, 34 patients with high myopia had phacoemulsification surgery. Although one eye received an acrylic intraocular lens (IOL) and CTR, other eye received only an IOL as control. PCO, within the capsulorhexis overlap, was documented by standardized digital retroillumination images at least 2 years post-operatively, and the percentage area of PCO was scored (scale 0%-100%) using the POCOman software system. The PCO score and the incidence of neodymium-doped yttrium aluminum garnet (Nd: YAG) capsulotomy of groups, and correlations between PCO score and presence of CTR, age, phaco time, refraction, and axial length (AL) were determined. Results: The mean time interval from surgery to PCO measurement was 43.4 ± 11.2 months for the eyes with a CTR and 43.1 ± 11.6 months for the controls (P = 0.91). The PCO score of the eyes with a CTR was significantly lower than in the controls (5.9 ± 4.3 vs. 22.3 ± 12.2, respectively; P < 0.001). There were statistically insignificant correlations between PCO score and pre-operative refraction (r = 0.02; P = 0.90), AL (r = 0.03; P = 0.80), phaco time (r = 0.11; P = 0.53), and patient′s age (r = 0.23; P = 0.55). No patient with a CTR had a Nd: YAG laser capsulotomy, but it was six in controls (P = 0.025). Conclusions: CTR implantation seems to be effective in reducing the PCO and Nd: YAG laser capsulotomy rates in high myopic eyes.

Statistical significance (p value) is a considerable measure when the manuscripts were reading or interpreting. On the other hand clinical importance, which is important at least as statistical significance, may not consider every time by authors or readers. In this manuscript some information related to an important measure, which indicates clinical importance effect size concept (ES), ES calculation and ES interpretation are presented. [PBS 2014; 4(1.000): 44-6]

Most of the studies related to health are conducted with samples. Selection of sampling may be performed with random or nonrandom sampling methods. Sampling methods may vary with respect to some characteristics of study population, researchers' aims and facilities. It is necessary to select samples by random sampling methods in order to provide the representativeness of the study population. The advantages and disadvantages of different sampling methods are presented in this manuscript which are used in psychiatry related articles. [PBS 2013; 3(1.000): 44-6]

The Chi square (?2) test is used to examine whether distributions of categorical variables differ from one another. The Chi Square statistic compares the counts of categorical responses between two (or more) independent groups. Pearson's chi-square test is most widely used of many chi-square test. A better approximation can be calculated by reducing the absolute value of each difference between observed and expected frequencies by 0.5; which is called Yates's correction for continuity. Fisher's exact test is employed when sample size is small in comparisions.

In statistics, binary logistic regression analysis is a regression model where the dependent variable is a dichotomous categorical variable. The binary logistic model is used to estimate the probability of a binary response based on one or more independent variables. Binary logistic regression makes use of one or more predictor variables that may be either continuous, ordinal or categorical. It is necessary to evaluate the goodness of fit when composing a model. Multicollinearity should be investigated between independent variables when a multivariate model is composing. Selection of the covariates and correct interpretation of the results are both important. It should be considered that a statistically significant model may not always be the correct model.

A meta-analysis a statistical method that integrates the quantitative findings from separate but similar studies and provides a numerical estimate of interest. A well conducted systematic review is very important before performing a successful meta-analysis. It is necessary to test heterogeneity between studies included in meta-analysis. Publication bias is a major problem in meta-analysis. So funnel plots should use to detect a possible publication bias. Sensitivity analysis is also an important statistical method to reduce the uncertainty. A forest plot will make easier to interpret the aggregate effect size obtained by combining all the studies included in meta-analysis. In conclusion, careful reviewing of published meta-analyses and a balanced assessment of their deficiencies is likely to become an increasingly important way of resolving uncertainty in healthcare practices.

Analysis of Covariance (ANCOVA) is a statistical method which is an extension of ANOVA that provides a way of statistically controlling the linear effect of variables one does not want to examine in a study. These extraneous variables are called covariates, or control variables. Covariates should be measured on an interval or ratio scale. ANCOVA, which combines regression analysis and analysis of variance (ANOVA), controls for the effects of covariate(s). There should be no multicollinearity between covariates. On the other hand there should be a linear association between dependent variable and covariate(s). If a good covariate is used in ANCOVA, then type 2 error decreases, in other words the power of the study increases.