Examples of 'non-parametric tests' in a sentence
Meaning of "non-parametric tests"
non-parametric tests - These are statistical tests that do not require specific assumptions about the underlying distribution of data. Non-parametric tests are used when data are not normally distributed or when the sample size is small. They provide an alternative to parametric tests, such as t-tests or ANOVA, and can be used to analyze categorical or ordinal data
How to use "non-parametric tests" in a sentence
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non-parametric tests
Non-parametric tests were used in this study.
For all analyses non-parametric tests were used.
Non-parametric tests and the problem of similarity of strategies.
Choosing between parametric and non-parametric tests.
Parametric and non-parametric tests were used as appropriate.
In the evaluation of dietary consumption variables, non-parametric tests were applied.
Non-parametric tests were performed to assess differences between groups.
Introduction to some non-parametric tests.
Non-parametric tests were used to compare between group differences.
Data were tabulated and analyzed by non-parametric tests and statistical significance was 5.
Non-parametric tests for group differences between categories were performed.
For secondary parameters parametric or non-parametric tests will be applied as appropriate.
Non-parametric tests were used due to the small sample size.
Since variables were not normally distributed, non-parametric tests were carried out.
Parametric and non-parametric tests were used in the analyses.
See also
Due to sample size and variables distribution, non-parametric tests were adopted.
Appropriate non-parametric tests may be applied for analysis.
In the case of heterogeneity of the variances, the non-parametric tests were employed.
Non-parametric tests were chosen due to the small sample size.
Data not presenting a normal distribution were assessed with non-parametric tests.
Finally, non-parametric tests are applied to detect critical levels of association.
Statistical analysis was performed using descriptive methods and non-parametric tests.
Therefore non-parametric tests were used for the subsequent analysis.
If continuous variables did not present in a normal distribution, non-parametric tests were applied.
The use of non-parametric tests was considered adequate for categorical variables.
Statistical analyses were performed using descriptive statistics and non-parametric tests.
Parametric and non-parametric tests were used for statistical analysis of results.
Group differences were analyzed with parametric or non-parametric tests when appropriate.
Non-parametric tests were used as the data did not demonstrate normal distribution.
Statistical comparisons are carried out with the aid of non-parametric tests.
Non-parametric tests were performed since each group consisted of eight replicate samples.
Variables with presumed continuous distribution were analyzed using non-parametric tests.
Non-parametric tests were used since the distribution of the variables was not normal.
Qualitative dada were analyzed by odds ratio, and ordinal data by non-parametric tests.
Non-parametric tests were used for statistical analysis, considering the nature of variables.
The results analysis for the measurements was performed by using non-parametric tests.
When normality can not be inferred, non-parametric tests or data transformation can be used.
Non-parametric tests relate to data that are flexible and do not follow a normal distribution.
Due to the small sample size of the study non-parametric tests were conducted.
Non-parametric tests were applied, since the dataset comprised a low sample size ten children.
The statistical analysis included the non-parametric tests of mann - whitney and kruskal.
Parametric and non-parametric tests were used for statistical analysis according to necessary model assumptions.
Considering the nature of the variables studied, non-parametric tests were utilized for statistical analysis.
These non-parametric tests do not assume that the data fit the normal distribution.
They implement a new volatility measure bipower variation measure and corresponding non-parametric tests for jumps.
Descriptive statistics and non-parametric tests were applied, due to data not being of normal distribution.
The results were treated statistically through the Wilcoxon and Mann-Whitney non-parametric tests.
Then we chose to use non-parametric tests because the data did not reach normal distribution.
Statistical analysis was performed using Microsoft Excel and software package EXStat for non-parametric tests.
Freidman 's non-parametric tests were used to analyze the ranked intensity for each test sample.
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Non-parametric tests were used in this study
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