Statistics unpaired test

Because bilberry has traditionally been used to treat diabetes, which is associated with alteration of lipid metabolism, the effects of a bilberry leaf extract on plasma glucose and triglycerides were studied in various rat models (1). The study preparation was made by percolation of bilberry leaf powder with ethanol 40% (Indena, Milano, Italy). All statistical comparisons were done using unpaired /-tests. 

Statistical analysis For statistical analysis of the behavioral tests an analysis of variance (two-way ANOVA) was used. For the symptomatology a Fisher exact probability test or an unpaired t-test with Welch s correction was used. In all tests p values <0.05 were considered significant. 

Statistics. The levels of significance between two sets of unpaired results were determined according to Student s t test. 

More generally the test statistic is constructed as the signal/noise (signal-to-noise) ratio or something akin to this. We will develop this methodology in relation the comparison of two independent means for a between-patient design. The resulting test is known as the unpaired t-test or the two-sample t-test. 

Using several different statistical methods, for example, an unpaired t-test, an analysis adjusted for centre effects, ANCOVA adjusting for centre and including baseline risk as a covariate, etc., and choosing that method which produces the smallest p-value is another form of multiplicity and is inappropriate. 

In the next section we will discuss Kaplan-Meier curves, which are used both to display the data and also to enable the calculation of summary statistics. We will then cover the logrank and Gehan-Wilcoxon tests which are simple two group comparisons for censored survival data (akin to the unpaired t-test), and then extend these ideas to incorporate centre effects and also allow the inclusion of baseline covariates. 

Safety analysis patient set was defined as all patients who received the Biod/VTs/o Batimastat OC stent, per-protocol analysis patient set was defined as all patients in the Safety analysis set who did not deviate from the protocol. Categorical variables were summarized using counts and percentages. Continuous variables were summarized using mean, standard deviation, minimum and maximum, and median for variable not showing a normal distribution. For comparison of subgroups, the unpaired two-tailed student s t-test was used. Results were considered statistically significant at P< 0.05. 

All values are expressed as mean SD. Statistical evaluation is carried out by two-tailed t-test for paired or unpaired observations. 

Data are expressed as the means SE. Statistical significance is assessed by two-tailed unpaired Student s t-test or one way analysis of variance (ANOVA) followed by either Dunnett s test for multiple comparisons vs. control or the Newman-Keuls test for all pair-wise comparisons. Tests indicating a value of P < 0.05 indicate a statistically significant difference between groups. 

Statistical analysis of the data is performed by means of the Student s t-test for paired or unpaired data, or by means of analysis of variance followed by the Tukey test. Statistical analysis of nonparametric data is made by the chi square test. 

Means standard error of the mean are calculated. Statistical evaluation is performed by using Student s t-test for paired or unpaired data. 

Mean values of TT, PT and FIT are calculated in dosage groups and vehicle controls. Statistical evaluation is performed by means of the unpaired Student s t-test. 

Mean values for aggregation in dosage groups are compared to the vehicle control groups (for rabbits control values before drug administration). Statistical significance is evaluated by means of the Student s t-test (paired for rabbits unpaired for others). 

Statistical significance is assessed by means of the unpaired Student s t-test. 

Thrombus score is expressed as median (minimum-maximum). Thrombus weight is given as mean SEM. For the statistical evaluation of the antithrombotic effect, the nonparametric U-Test of Mann and Whitney (thrombus score) or Student s t-test for unpaired samples (thrombus weight) is used. Significance is expressed as p < 0.05. 

A statistical test that is often appropriate for comparing two groups in terms of a quantitative outcome measure is the unpaired t-test. Other assumptions underpin the use of a t-test (see later) and it is therefore sometimes desirable to use one of the tests primarily intended for use on ordinal data even if the data are quantitative. 

The unpaired t-test is an example of a parametric method, which means that it is based on the assumption that the two samples are taken from normal, or approximately normal distributions. Generally, parametric tests should be used where possible because they are more powerful (effectively, more sensitive) than the alternative non-parametric methods [32]. However, significance levels obtained from parametric tests may be inaccurate, and the true power of the test may decrease, if the assumption of normality is poor. The non-parametric alternative to the unpaired t-test is the Mann-Whitney test [32]. In this test, a rank is assigned to each observation (1 = smallest, 2 = next smallest, etc.), and the test statistic is computed from these ranks. Obviously, the test is less sensitive to departures from normality, such as the presence of outliers, since, for example, the rank assigned to the smallest observation will always be 1, no matter how small that observation is. 

Statistically significant (Student s t-test for unpaired data) at p<0.05. 

Results were expressed as mean+SEM. Statistical analyses were obtained by using unpaired t test or analysis of variance (ANOVA). A p value less than 0.05 was considered as statistically significant. 

Most researchers who have worked with discrete event simulation are familiar with classical statistical analysis. By classical, we mean those tests that deal with assessing differences in means or that perform correlation analysis. Included in these tests are statistic procedmes such as t-tests (paired and unpaired), analysis of variance (univariate and multivariate), factor analysis, linear regression (in its various forms ordinary least squares, LOGIT, PROBIT, and robust regression) and non-parametric tests. 

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