0
The answer depends on what is being tested: the t-test, F-test, Chi-square, Z-test are all commonly used with the Normal distribution. There are many others.
Wiki User
∙ 9y ago
Add your answer:
Earn +20 pts
How do you interpret a KSL or Kolmogorov-Smirnov-Lilliefors test?
If the test result is significant (Lower than or equal to 0.05) = The data is not normally distributed... If the test result is not significant (Higher than 0.05) = The data is normally distributed... This synchronize with the Statistical Hypothesis Assumption (Ho and Ha) Ho means "Nothing Happen" and Ha means "Something Happen" then for KSL and Shapiro Wilk test of normality assumption also.... If the test result reject Ha and accept Ho means "NOTHING HAPPEN" to data or the data is normally distributed but if the result reject Ho and accept Ha means "SOMETHING HAPPEN" to data or in this case the data is NOT normally distributed. Dr.Tanarat Thiengkamol (send2nude@gmail.com)
How do you know whether data requires you to use a parametric or non parametric test?
Parametric tests assume that your data are normally distributed (i.e. follow a classic bell-shaped "Gaussian" curve). Non-parametric tests make no assumption about the shape of the distribution.
When doing a t-test what does normal distributed in population mean?
It means that the random variable of interest is Normally distributed and so the t-distribution is an appropriate distribution for the test rather than just an approximation.
What are examples of parametric and nonparametric statistical tests?
Parametric statistical tests assume that your data are normally distributed (follow a classic bell-shaped curve). An example of a parametric statistical test is the Student's t-test.Non-parametric tests make no such assumption. An example of a non-parametric statistical test is the Sign Test.
How do one determine when a data is normally distributed?
Use the Kolmogorov Smirnoff goodness-of-fit test. A normal distribution is a bell shaped curve, which is nearly symmetrica. It looks like an upside down bell. It can be squished low (platykurtic) or pulled high and skinny (leptokurtic) but it is still bell shaped and symmetrical. A mathematical test is to use the pearson's skew. If the pearson's skew is between 0 and 0.49, then the data is a non-problematic or normally distributed. If it is greater than 0.50, then it is not a normal distribution so one cannot treat it as such. The pearson's skew equation is skew p= (3 (mean - median)) / (SD(x) SD(y))
How do you interpret a KSL or Kolmogorov-Smirnov-Lilliefors test?
If the test result is significant (Lower than or equal to 0.05) = The data is not normally distributed... If the test result is not significant (Higher than 0.05) = The data is normally distributed... This synchronize with the Statistical Hypothesis Assumption (Ho and Ha) Ho means "Nothing Happen" and Ha means "Something Happen" then for KSL and Shapiro Wilk test of normality assumption also.... If the test result reject Ha and accept Ho means "NOTHING HAPPEN" to data or the data is normally distributed but if the result reject Ho and accept Ha means "SOMETHING HAPPEN" to data or in this case the data is NOT normally distributed. Dr.Tanarat Thiengkamol (send2nude@gmail.com)
What are the assumptions underlying the two sample t test?
The assumptions of a two-sample t-test are: Each sample come from a normally distributed population. Both populations have equal variances. The data are sampled independently from each population.
How do you know whether data requires you to use a parametric or non parametric test?
Parametric tests assume that your data are normally distributed (i.e. follow a classic bell-shaped "Gaussian" curve). Non-parametric tests make no assumption about the shape of the distribution.
When doing a t-test what does normal distributed in population mean?
It means that the random variable of interest is Normally distributed and so the t-distribution is an appropriate distribution for the test rather than just an approximation.
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 are examples of parametric and nonparametric statistical tests?
Parametric statistical tests assume that your data are normally distributed (follow a classic bell-shaped curve). An example of a parametric statistical test is the Student's t-test.Non-parametric tests make no such assumption. An example of a non-parametric statistical test is the Sign Test.
How do you find out what books you have taken an Accelerated Reader test on?
Once youre into your account, where you would normally click take a test clickview quizes taken and go from there.
Assumption of one-way analysis of variance?
The results of a one-way ANOVA can be considered reliable as long as the following as The results of a one-way ANOVA can be considered reliable as long as the following assumptions are met: * Response variable must be normally distributed (or approximately normally distributed). * Samples are independent. * Variances of populations are equal. * The sample is a Simple Random Sample (SRS). ANOVA is a relatively robust procedure with respect to violations of the normality assumption (Kirk, 1995) If data are ordinal, a non-parametric alternative to this test should be used - Kruskal-Wallis one-way analysis of variance. sumptions are met: * Response variable must be normally distributed (or approximately normally distributed). * Samples are independent. * Variances of populations are equal. * The sample is a Simple Random Sample (SRS). ANOVA is a relatively robust procedure with respect to violations of the normality assumption (Kirk, 1995) If data are ordinal, a non-parametric alternative to this test should be used - Kruskal-Wallis one-way analysis of variance
How do one determine when a data is normally distributed?
Use the Kolmogorov Smirnoff goodness-of-fit test. A normal distribution is a bell shaped curve, which is nearly symmetrica. It looks like an upside down bell. It can be squished low (platykurtic) or pulled high and skinny (leptokurtic) but it is still bell shaped and symmetrical. A mathematical test is to use the pearson's skew. If the pearson's skew is between 0 and 0.49, then the data is a non-problematic or normally distributed. If it is greater than 0.50, then it is not a normal distribution so one cannot treat it as such. The pearson's skew equation is skew p= (3 (mean - median)) / (SD(x) SD(y))
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.
What are t test and F test In which situation these are applied?
Both are parametric test. The t-test uses a test statistic that is related to the sample mean(s) and is used to compare that with the mean of another sample or some population. The F-test uses a test statistic that is related to the sample variance and is used to compare that with the variance of another sample or some population. Both tests require identical independently distributed random variables. This ensures that the relevant test statistics are approximately normally distributed.
Explain in brief about optimal test?
OptimalTest is a multi-enterprise software solution that delivers actionable test data to support management decisions. In today's distributed semiconductor manufacturing environment, it is common for devices to be designed in one place, fabricated (manufactured) in another and tested in still another -- and sometimes in more than one other place. These operations are globally distributed and data is generated off various types of testers. The tools delivered to the market by OptimalTest provide for: * transparency of data across the multi-enterprise, distributed supply chain; * actionable test data to support management decision making. The OptimalTest tool suite delivers a positive return on investment in terms of Yield (time to yield and yield recovery), Quality, Reliability and Cost of Test.
