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What are the three criteria for parametric test to be used?
normal distiribution n>30 numeratical data
Is z test is parametric?
Yes, the z-test is a parametric statistical test. It assumes that the underlying data follows a normal distribution and requires that the population standard deviation is known. This test is typically used to determine if there is a significant difference between sample and population means or between the means of two samples, making it suitable for normally distributed interval data.
How do you compare parametric and non-parametric analysis?
In parametric analysis the underlying distributions of the variables are described by parameters. These may be known or it may be possible to estimate them from the observed data. In non-parametric analyses, the parameters are not used - either because they cannot be derived or because the tests do not require them.
When is the Chi Square Test used?
To test how well observations agree with some expected distribution. The latter is often non-parametric so that tests based on the Gaussian (Normal) distribution are not appropriate.
What statistical test should be used to determine if the average scores of two independent groups?
To determine if the average scores of two independent groups are significantly different, you should use an independent samples t-test. This test compares the means of the two groups and assesses whether any observed difference is statistically significant. It assumes that the data is normally distributed and that the variances of the two groups are equal (or approximately so). If the assumptions are not met, a non-parametric alternative, such as the Mann-Whitney U test, can be used.
What are the three criteria for parametric test to be used?
normal distiribution n>30 numeratical data
When should the Kruskal-Wallis test be used?
The Kruskal-Wallis test should be used when you have three or more independent groups and want to compare the medians of non-normally distributed data. It is a non-parametric alternative to the parametric ANOVA test and can be applied when the assumptions for ANOVA, such as normality and homogeneity of variances, are violated. The Kruskal-Wallis test is particularly useful when working with ordinal or skewed interval/ratio data.
What is a parametric test used to compare the means of two groups?
t-test
What are the types of hypothesis testing?
There are several types of hypothesis testing, primarily categorized into two main types: parametric and non-parametric tests. Parametric tests, such as t-tests and ANOVA, assume that the data follows a specific distribution (usually normal). Non-parametric tests, like the Mann-Whitney U test or the Kruskal-Wallis test, do not rely on these assumptions and are used when the data doesn't meet the criteria for parametric testing. Additionally, hypothesis tests can be classified as one-tailed or two-tailed, depending on whether the hypothesis specifies a direction of the effect or not.
Parametric tests can be used with any type of data as the dependent variable?
true
What is the difference between parametric and nonparametric statistical tests?
Parametric are the usual tests you learn about. Non-parametric tests are used when something is very "wrong" with your data--usually that they are very non-normally distributed, or N is very small. There are a variety of ways of approaching non-parametric statistics; often they involve either rank-ordering the data, or "Monte-Carlo" random sampling or exhaustive sampling from the data set. The whole idea with non-parametrics is that since you can't assume that the usual distribution holds (e.g., the X² distribution for the X² test, normal distribution for t-test, etc.), you use the calculated statistic but apply a new test to it based only on the data set itself.
What is the difference between parametric and nonparametric statistical tests in Health care?
Parametric tests draw conclusions based on the data that are drawn from populations that have certain distributions. Non-parametric tests draw fewer conclusions about the data set. The majority of elementary statistical methods are parametric because they generally have larger statistical outcomes. However, if the necessary conclusions cannot be drawn about a data set, non-parametric tests are then used.
What is the Kruskal-Wallis test, and when is it appropriate to use it?
The Kruskal-Wallis test is a non-parametric statistical test used to compare the medians of three or more independent groups. It is appropriate to use when the data violate the assumptions of parametric tests, such as ANOVA, such as non-normality or unequal variances. It is commonly used when analyzing ordinal or continuous data that are not normally distributed. You can get expert assistance also from various online consultancies such as SPSS-Tutor, Silverlake Consult, etc.
How do you compare parametric and non-parametric analysis?
In parametric analysis the underlying distributions of the variables are described by parameters. These may be known or it may be possible to estimate them from the observed data. In non-parametric analyses, the parameters are not used - either because they cannot be derived or because the tests do not require them.
What are reliability indicators?
Reliability indicators are measures used to assess the consistency and stability of data or results. Common reliability indicators include test-retest reliability, internal consistency (Cronbach's alpha), inter-rater reliability, and split-half reliability. These indicators help researchers determine the trustworthiness and accuracy of their measurements.
Distinguish between parameteric statistics and non - parameteric statistics?
The simplest answer is that parametric statistics are based on numerical data from which descriptive statistics can be calculated, while non-parametric statistics are based on categorical data. Takes two example questions: 1) Do men live longer than women, and 2), are men or women more likely to be statisticians. In the first example, you can calculate the average life span of both men and women and then compare the two averages. This is a parametric test. But in the second, you cannot calculate an average between "man" and "woman" or between "statistician" or "non-statistician." As there is no numerical data to work with, this would be a non-parametric test. The difference is vitally important. Because inferential statistics require numerical data, it is possible to estimate how accurate a parametric test on a sample is compared to the relevant population. However, it is not possible to make this estimation with non-parametric statistics. So while non-parametric tests are still used in many studies, they are often regarded as less conclusive than parametric statistics. However, the ability to generalize sample results to a population is based on more than just inferential statistics. With careful adherence to accepted random sampling, sample size, and data collection conventions, non-parametric results can still be generalizable. It is just that the accuracy of that generalization can not be statistically verified.
If a test yields consistent results every time it is used, it has a high degree of?
reliability
