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How is the null hypothesis of the independent samples t test verbalized?
The null hypothesis of the independent samples t-test is verbalized by either accepting or rejecting it due to the value of the t-test. If the value is less than 0.05 it is accepted and greater than 0.05 is rejecting it.
If a researcher conducts a t-test on a single group mean and if df equals 19 how many subjects were in the sample?
20
Can a single t-test compare the means of 3 or more groups?
No, don't use a single t-test to compare the means of 3 or more groups. Use ANOVA.
When using a one-tailed test and a sample size of 24 what is the probability of a value being to the left of a number that has a student-t statistic of -2.50?
26.5
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
When independent sample t test is used?
The Independent Samples T Test compares the mean scores of two groups on a given variable.
What are the assumptions underlying the two-sample t-test?
normal, SRS, independent normal, SRS, independent
Difference scores are usually used with a. a t test for a single sample b. a t test for dependent means c. a t test for independent means d. all of the above?
d
Is a paired t-test more powerful than a a test between independent samples a A paired t- test is more powerful because it utilizes information b an independent sample t0-te?
paired t-test is more powerful because it utilizes information
When to use t-test?
You use the t-test when the population standard deviation is not known and estimated by the sample standard deviation. (1) To test hypothesis about the population mean (2) To test whether the means of two independent samples are different. (3) To test whether the means of two dependent samples are different. (4) To construct a confidence interval for the population mean.
What type of t tests should be used for determining the difference in students computer anxiety based on whether or not they own computers?
independent sample t test
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.
Why would you use a z test rather than a t test?
When the sample size is greater than 30
Why would you use a t test rather than a z test?
When the sample size is greater than 30
What is the t test?
The t-test is a statistical method used to determine if there is a significant difference between the means of two groups. It is commonly applied in hypothesis testing to compare sample data against a population or between two sample groups. The t-test accounts for variability and sample size, allowing researchers to infer whether observed differences are likely due to chance. There are different types of t-tests, including independent, paired, and one-sample t-tests, each suited for specific study designs.
Is a t-test a parametric study?
Yes, it is. The one sample t-test is a study of the parameter population-mean. You can also use the t-test to test for the difference between two population means (both parameters).
What is the relation between a t-test value and the sample variance?
The t-test value is calculated using the sample mean, the population mean, and the sample standard deviation (which is derived from the sample variance). Specifically, the formula for the t-test statistic incorporates the sample variance in the denominator, adjusting for sample size through the standard error. A smaller sample variance typically results in a larger t-test value, indicating a greater difference between the sample mean and the population mean relative to the variability in the sample data. Thus, the relationship is that the t-test value reflects how the sample variance influences the significance of the observed differences.
