IBM SPSS Sample Power V3.0.1

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There are actually two types of errors: the error of accepting H1 when it is not true in the population; this is called a Type I error; and is a false positive. The alpha defines the probability of a Type I error. Type I errors can happen for many reasons, from poor sampling that results in an experimental sample quite different from the population, to other mistakes occurring in the design stage or implementation of the research procedures. It is also possible to make an erroneous decision in the opposite direction; by incorrectly rejecting H1 and thus wrongly accepting H0. This is called a Type II error (or a false negative). The β defines the probability of a Type II error. The most common reason for this type of error is small sample size, especially when combined with moderately low or low effect sizes. Both small sample sizes and low effect sizes reduce the power in the study.

Nomogram for sample size and power, for comparing two groups of equal size. Gaussian distributions assumed. Standardized difference (effect size) and aimed power values are initially selected on the nomogram. The line connecting these values cross the significance level region of the nomogram. The intercept at the appropriate significance value presents the required sample size for the study. In the above example, for effect size = 1, power = 0.8 and alpha value = 0.05, the sample size is found to be 30. (Adapted from reference 16).

In recent years, numerous software and websites have been developed which can successfully calculate sample size in various study types. Some of the important software and websites are listed in Table 2 and are evaluated based both on the remarks stated in the literature and on our own experience, with respect to the content, ease of use, and cost (31, 32). G-Power, R, and Piface stand out among the listed software in terms of being free-to use. G-Power is a free-to use tool that be used to calculate statistical power for many different t-tests, F-tests, χ2 tests, z-tests and some exact tests. R is an open source programming language which can be tailored to meet individual statistical needs, by adding specific program modules called packages onto a specific base program. Piface is a java application specifically designed for sample size estimation and post-hoc power analysis. The most professional software is PASS (Power Analysis and Sample Size). With PASS, it is possible to analyse sample size and power for approximately 200 different study types. In addition, many websites provide substantial aid in calculating power and sample size, basing their methodology on scientific literature.

The Figure 3 shows the change of sample size depending on the ES changes (0.2, 1 and 2.5, respectively) provided that the power remains constant at 0.8. Arguably, case 3 is particularly common in pre-clinical studies, cell culture, and animal studies (usually 5-10 samples in animal studies or 3-12 samples in cell culture studies), while case 2 is more common in clinical studies. In clinical, epidemiological or meta-analysis studies, where the sample size is very large; case 1, which emphasizes the importance of smaller effects, is more commonly observed (33).

In Figure 4, case 4 exemplifies the change in power and ES values when the sample size is kept constant (i.e. as low as 8). As can be seen here, in studies with low ES, working with few samples will mean waste of time, redundant processing, or unnecessary use of laboratory animals.

Relationship between effect size and power. Two different cases are schematized where the sample size is kept constant either at 8 or at 30. When the sample size is kept constant, the power of the study decreases as the effect size decreases. When the effect size is 2.5, even 8 samples are sufficient to obtain power = ~0.8. When the effect size is 1, increasing sample size from 8 to 30 significantly increases the power of the study. Yet, even 30 samples are not sufficient to reach a significant power value if effect size is as low as 0.2.

Likewise, case 5 exemplifies the situation where the sample size is kept constant at 30. In this case, it is important to note that when ES is 1, the power of the study will be around 0.8. Some statisticians arbitrarily regard 30 as a critical sample size. However, case 5 clearly demonstrates that it is essential not to underestimate the importance of ES, while deciding on the sample size.

Preliminary information about the hypothesis is eminently important to calculate the sample size at intended power. Usually, this is accomplished by determining the effect size from the results of a previous study or a preliminary study. There are software available which can calculate sample size using the effect size

In a compilation where Ricci et al. reviewed 15 studies involving animal models, it was noted that the sample size used was 10 in average (between 6 and 18), however, no formal power analysis was reported by any of the groups. It was striking that, all studies included in the review have used parametric analysis without prior normality testing (i.e. Shapiro-Wilk) to justify their statistical methodology (46).

Case-control association studies for single nucleotide polymorphisms (SNPs) may be facilitated using OSSE web site ( -star.edu.sg/). As an example, let us assume the minor allele frequencies of an SNP in cases and controls are approximately 15% and 7% respectively. To have a power of 0.8 with 0.05 significance, the study is required to include 239 samples both for cases and controls, adding up to 578 samples in total (Figure 6).

Additionally, comparison studies might be carried out in clinical laboratories for other purposes; such as inter-device, where usage of relatively few samples is suggested to be sufficient. For method comparison studies to be conducted using patient samples; sample size estimation, and power analysis methodologies, in addition to the required number of replicates are defined in CLSI document EP31-A-IR. The critical point here is to know the values of constant difference, within-run standard deviation, and total sample standard deviation (69). While studies that compare devices having high analytical performance would suffice lower sample size; studies comparing devices with lower analytical performance would require higher sample size.

Standards for reporting of diagnostic accuracy studies (STARD) checklist may be followed for diagnostic studies. It is a powerful checklist whose application is explained in detail by Cohen et al. and Flaubaut et al. (85, 86). This document suggests that, readers demand to understand the anticipated precision and power of the study and whether authors were successful in recruiting the sufficient number of participants; therefore it is critical for the authors to explain the intended sample size of their study and how it was determined. For this reason, in diagnostic and prognostic studies, sample size and power should clearly be stated.

The chart below -created in G*Power- shows how required sample size and power are related to effect size.if(typeof ez_ad_units != 'undefined'){ez_ad_units.push([[300,250],'spss_tutorials_com-large-mobile-banner-2','ezslot_13',121,'0','0'])};__ez_fad_position('div-gpt-ad-spss_tutorials_com-large-mobile-banner-2-0');

\(f^2\) is useful for computing the power and/or required sample size for a regression model or individual predictor. However, these also depend on the number of predictors involved. The figure below shows how required sample size depends on required power and estimated (population) effect size for a multiple regression model with 3 predictors.

Interestingly, although our results revealed a decreased lactoferrin concentration and lactoferrin flow rate in the OLP patients undergoing topical 0.1% FAO treatment compared with the other groups, there were no significant differences in salivary flow rate, lactoferrin concentration or salivary lactoferrin flow rate between the groups. However, these decreases did not result in a significant difference in the Candida count between these groups. Therefore, the limitations of the present study should be considered. Although the sample size in this study had sufficient statistical power according to the sample size calculation, the small sample size in each group may be a key limitation. Furthermore, the limited duration of the present study is also a concern. Thus, we suggest that these findings should be further investigated in future studies with a larger sample size and longer duration. The progress in further research will be helpful in our understanding the mechanisms and the biological role of salivary lactoferrin in inhibiting the occurrence of oral candidiasis in OLP patients undergoing topical steroid therapy. In the future, an advanced understanding of the pathogenesis of oral candidiasis may aid in developing new prevention methods that decrease the risk of oral candidiasis in patients undergoing topical steroid treatment. 2b1af7f3a8