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Assumptions of the t test and F test for independent means
The t test for independent means and the F test for independent means are based upon several assumptions about the nature of reality. If these assumptions are not true then the results of your analysis could be seriously flawed without you realizing it.
1) The scores in the various groups are independent. This means that one person's score does not affect any other person's score, they are independent of each other. An obvious violation of this assumption would occur if you had more than one score per person in your data set, as those scores come from the same person they are obviously connected in an important way (i.e. are not independent). The statistical analysis will be seriously flawed if the scores are not independent.
2) The populations are normally distributed. By 'populations' were are referring to the populations represented by each group in the experiment. The t and F tests were built upon the assumption that each population in the experiment is normally distributed. This assumption ends up only being important if you have a small number of scores in teach group. If N=30 in each group then you don't need to worry about whether or not this assumption is true. If you have reason to think that the populations are somewhat close to being normally distributed then you can have less than 30 scores in each group.
3) All the populations have the same variance. The t and F tests were also built upon the assumption that every population in the experiment have identical variances. This assumption ends up only being important if you have widely different N in each group. If you have roughly the same number of scores in each group then you don't need to worry about whether or not this assumption is true.
Limitations
The t and F tests for independent means only examine means, they have virtually nothing to say about individual scores. It is important to keep track of the fact that our conclusions are about means, not about individuals. So, if we run a study with gender as an independent variable, and get a statistically significant result, then we can say that the mean of males differs from the mean of females in terms of the dependent variable, but we cannot say that any particular male will have a higher or lower score than any particular female.
What else can I help you with?
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.
What are the assumptions underlying the two-sample t-test?
normal, SRS, independent normal, SRS, independent
What are the Limitations of the t-test in statistics?
Two limitations of a t-test are you can only use one factor at a time and you can only use two levels at a time. You have to watch out for the Type 1 error because it increases with simultaneous tests.
What is a t-test?
a t test is used inplace of a z-test when the population standard deviation is unknown.
What is t Test?
t-test is the statistical test used to find the difference of mean between two groups
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.
What are the assumptions underlying the two-sample t-test?
normal, SRS, independent normal, SRS, independent
What are the Limitations of the t-test in statistics?
Two limitations of a t-test are you can only use one factor at a time and you can only use two levels at a time. You have to watch out for the Type 1 error because it increases with simultaneous tests.
What is parametric test?
A parametric test is a type of statistical test that makes certain assumptions about the parameters of the population distribution from which the samples are drawn. These tests typically assume that the data follows a normal distribution and that variances are equal across groups. Common examples include t-tests and ANOVA. Parametric tests are generally more powerful than non-parametric tests when the assumptions are met.
What are the limitations of t-test?
The t-test has several limitations, including its assumption of normally distributed data, which can lead to inaccurate results with small sample sizes or non-normal distributions. It also assumes homogeneity of variance, meaning that the variances of the groups being compared should be similar. Additionally, the t-test is sensitive to outliers, which can skew results and affect the validity of the conclusions drawn. Finally, it only compares the means of two groups, limiting its applicability for studies involving multiple groups or complex relationships.
To conduct a t test for independent means ideally the populations should be both normally distributed and have the same?
The other assumptions are listed in the related link. The answer you are looking for is the same variance or standard deviation.
When to t test z test t interval z interval etc?
Use a t-test when comparing the means of two groups, especially when the sample size is small (n < 30) and the population standard deviation is unknown. A z-test is appropriate for large sample sizes (n ≥ 30) or when the population standard deviation is known. For confidence intervals, use a t-interval for smaller samples with unknown population standard deviation, and a z-interval for larger samples or known population standard deviation. Always check if the data meets the assumptions for each test before proceeding.
How is paired t-test different from a general t-test with two samples?
In a general t-test, there is no relationship between the members of one sample and the other. In a paired t-test they are connected in some way so that they are likely to give similar outcomes. This means that more of the difference between them can be attributed to the "treatment".
What are the limitations of two sample independent t-test?
The two-sample independent t-test has several limitations, including the assumption of normality, which may not hold true for smaller sample sizes or non-normally distributed data. It also assumes homogeneity of variances, meaning that the variances of the two groups being compared should be equal; violations can affect the test's validity. Additionally, the test is sensitive to outliers, which can skew results, and it is only applicable for comparing means between two groups, limiting its use in more complex experimental designs.
Is T test similar to Z test?
no t test is similar to z test because t test ie used for unknown observation and z is for the medicne
What are the advantage and disadvantage of student t test?
The Student's t-test is advantageous because it is simple to use, requires minimal assumptions about the data, and is effective for comparing means between two groups, especially with small sample sizes. However, its main disadvantage is that it assumes the data are normally distributed; violations of this assumption can lead to inaccurate results. Additionally, the t-test is limited to comparing only two groups at a time, which can be a drawback when analyzing multiple groups simultaneously.
what are the answers for the test?
Some are T or F, others are A,B,C or D
