Which statistical test should be used to compare treatment groups?
If the frequency of success in two treatment groups is to be compared, Fisher’s exact test is the correct statistical test, particularly with small samples. For large samples (about N> 60), the chi-square test can also be used [Table 1]. Paired samples
Why do we select statistical tests before the study begins?
The selection of the statistical test before the study begins ensures that the study results do not influence the test selection. The decision for a statistical test is based on the scientific question to be answered, the data structure and the study design.
What is the t-test for before and after comparison?
For before and after comparison on the same group of people, a paired sample t-test where the dependent variable is continuous (e.g. systolic blood pressure before and after treatment) and McNemar's test where the dependent variable is dichotomous (e.g. hypertension yes / no before and after treatment).
What is a statistical test used for?
A statistical test is used to compare the results of the endpoint under different test conditions (such as treatments). There are often two therapies. If results can be obtained for each patient under all experimental conditions, the study design is paired (dependent).
What is the best test to use when we are dealing with a before and after data wherein there is only one group?
For before and after comparison for continuous variables (e.g. systolic blood pressure before and after treatment) then a paired t-test may be appropriate. If the data is not normally distributed then an alternative would be the Wilcoxon Sign Rank test.
How would you decide which statistical test to use?
For a statistical test to be valid, your sample size needs to be large enough to approximate the true distribution of the population being studied. To determine which statistical test to use, you need to know: whether your data meets certain assumptions. the types of variables that you're dealing with.
What is the best statistical test to compare two groups?
The two most widely used statistical techniques for comparing two groups, where the measurements of the groups are normally distributed, are the Independent Group t-test and the Paired t-test.
When do you use ANOVA or t-test?
The Student's t test is used to compare the means between two groups, whereas ANOVA is used to compare the means among three or more groups.
When do you use chi-square vs t-test?
a t-test is to simply look at the types of variables you are working with. If you have two variables that are both categorical, i.e. they can be placed in categories like male, female and republican, democrat, independent, then you should use a chi-square test.
What is chi-square test used for?
A chi-square test is a statistical test used to compare observed results with expected results. The purpose of this test is to determine if a difference between observed data and expected data is due to chance, or if it is due to a relationship between the variables you are studying.
When do you use the Mann Whitney U test?
The Mann-Whitney U test is used to compare whether there is a difference in the dependent variable for two independent groups. It compares whether the distribution of the dependent variable is the same for the two groups and therefore from the same population.
What is at test and Z-test?
Content: T-test Vs Z-test T-test refers to a type of parametric test that is applied to identify, how the means of two sets of data differ from one another when variance is not given. Z-test implies a hypothesis test which ascertains if the means of two datasets are different from each other when variance is given.
When do you use independent t-test?
The independent t-test is used when you have two separate groups of individuals or cases in a between-participants design (for example: male vs female; experimental vs control group).
What is the difference between F-test and ANOVA?
ANOVA separates the within group variance from the between group variance and the F-test is the ratio of the mean squared error between these two groups.
What is Chi-Square t-test and ANOVA?
Chi-square test is used on contingency tables and more appropriate when the variable you want to test across different groups is categorical. It compares observed with expected counts. Both t test and ANOVA are used to compare continuous variables across groups.
What is the difference between t-test and F-test?
T-test is a univariate hypothesis test, that is applied when standard deviation is not known and the sample size is small. F-test is statistical test, that determines the equality of the variances of the two normal populations.
Popular Answers (1)
This depends on the data (continuous versus binary versus categorical etc.).
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I think a paired t -test might be appropriate here. However, you also should consider your sample size. If the sample size is not big, you might consider to do a nonparametric approach, for example, sign test for paired comparison. This sign test for paired comparison could be done using, for example, R.
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What is the statistical test I can use for the pre-test post-test control group research design?
What is statistical test?
They can be used to: determine whether a predictor variable has a statistically significant relationship with an outcome variable. estimate the difference between two or more groups. Statistical tests assume a null hypothesis of no relationship or no difference between groups.
What do you need to know to determine which statistical test to use?
To determine which statistical test to use, you need to know: whether your data meets certain assumptions. the types of variables that you’re dealing with.
What happens if the test statistic is less extreme than the one calculated from the null hypothesis?
If the value of the test statistic is less extreme than the one calculated from the null hypothesis, then you can infer no statistically significant relationship between the predictor and outcome variables.
What happens if you don't meet the assumptions of normality or homogeneity of variance?
If your data do not meet the assumptions of normality or homogeneity of variance, you may be able to perform a nonparametric statistical test, which allows you to make comparisons without any assumptions about the data distribution.
What is statistical significance?
Statistical significance is a term used by researchers to state that it is unlikely their observations could have occurred under the null hypothesis of a statistical test. Significance is usually denoted by a p -value, or probability value.
What happens if you don't meet the assumptions of nonparametric statistics?
the data are independent. If your data does not meet these assumptions you might still be able to use a nonparametric statistical test, which have fewer requirements but also make weaker inferences.
Why are non-parametric tests useful?
Non-parametric tests don’t make as many assumptions about the data , and are useful when one or more of the common statistical assumptions are violated. However, the inferences they make aren’t as strong as with parametric tests.
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You should use a mixed model to analyse these data. Mixed models let you specify a random effect, in your case the different subjects. The model will "understand" that each subject represents a cohesive block of measures, and should help you with the differences between them.
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What statistical test to use in pre and post test for one group design?
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You may try a simple analysis of variance or for more accurate results you can use a structural equation modeling approach.
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The appropriate statistical test depends on what your research question is. Consider repeated measures ANCOVA or repeated measures ANOVA. You would also not need to do separate t-tests because you could simply analyze the main effects of the ANOVA or ANCOVA to examine the effect of the intervention.
Similar questions and discussions
What is the statistical test I can use for the pre-test post-test control group research design?
Why is it important to select a statistical test before a study begins?
The selection of the statistical test before the study begins ensures that the study results do not influence the test selection. The decision for a statistical test is based on the scientific question to be answered, the data structure and the study design.
What is statistical testing?
Statistical tests are mathematical tools for analyzing quantitative data generated in a research study. The multitude of statistical tests makes a researcher difficult to remember which statistical test to use in which condition. There are various points which one needs to ponder upon while choosing a statistical test.
Is there a hypothesis in a prevalence study?
In some cases there is no hypothesis; the investigator just wants to “see what is there”. For example, in a prevalence study, there is no hypothesis to test , and the size of the study is determined by how accurately the investigator wants to determine the prevalence.
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Jon Torrez I understand that. what I'm saying is that you performed many experiments when you should have performed one, i.e. fix the time before the surgery and the time after surgery and then do 1 paired t-test on the results. Then you would have something potentially worth reporting. as it is now the time goes all over the place.
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Your experimental design, to use a technical term, is messed up because time could be a factor and your design doesn't include it. A POOR way to proceed would be to look at all the patients that have 1 year data, 2 years data, etc and see what happened. this would in no way satisfy the requirements of a clinical trial of your treatment.
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Which statistical test will be appropriate for three and more than three drug treatments?
