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tzx9633 said:
how about ##t(50) ## ? can i use this ?Ray Vickson said:
You tell me.tzx9633 said:
A t-value, also known as a t-statistic, is a measure of the difference between a sample mean and a population mean, adjusted for sample size and variability. It is used in hypothesis testing to determine the significance of a sample mean. It is important because it helps researchers determine if the results of a study are due to chance or if they are actually meaningful.
A t-value is calculated by taking the difference between the sample mean and the population mean, dividing it by the standard deviation of the sample, and then multiplying it by the square root of the sample size. This formula is represented as t = (x̄ - μ) / (s / √n), where x̄ is the sample mean, μ is the population mean, s is the standard deviation of the sample, and n is the sample size.
A t-distribution table is a mathematical table that provides critical values for the t-distribution, which is a probability distribution used in statistical analysis. It is used to determine the probability of obtaining a certain t-value or a more extreme value under the null hypothesis.
A t-distribution table should be used when conducting a hypothesis test for a small sample size (typically less than 30) and when the population standard deviation is unknown. It is also used when the data does not follow a normal distribution.
To use a t-distribution table, first determine the degrees of freedom, which is equal to the sample size minus 1. Then, locate the row that corresponds to the degrees of freedom and the column that corresponds to the desired level of significance. The value at the intersection of the row and column will be the critical t-value. If your calculated t-value is greater than the critical t-value, then the results are considered statistically significant.
