Suppose you conduct a t test on mean of Normal population: 𝐻0:𝜇=100 vs 𝐻1:𝜇>100. If sample size is 15 and test statistic t0 = 0.89, then using t table the P-value range is found to be

To find the P-value for a t-test, you need to follow these steps:

1. Identify the degrees of freedom (df): In this case, the degrees of freedom is n-1, where n is the sample size. So, df = 15 - 1 = 14.

2. Look up the t-value in the t-distribution table: You need to find the t-value of 0.89 under the row of df=14. However, t-tables usually only provide the area to the left of the t-value (i.e., the cumulative probability from negative infinity to the t-value). Since we are looking for the area to the right of the t-value (because the alternative hypothesis is 𝜇>100), we need to subtract the cumulative probability from 1 to get the P-value.

3. Find the P-value: The t-table will give you a range of probabilities for a t-value of 0.89 with df=14. Since the t-table only provides the cumulative probability from negative infinity to the t-value, you need to subtract each of these probabilities from 1 to get the range of P-values.

Please note that the exact P-value depends on the specific t-table you are using, as different tables may have slightly different probabilities for the same t-value and degrees of freedom.

To find the P-value for a t-test, you need to follow these steps:

1. Identify the degrees of freedom (df): In this case, the degrees of freedom is n-1, where n is the sample size. So, df = 15 - 1 = 14.

2. Look up the test statistic (t0) in the t-distribution table: You are given that t0 = 0.89. You need to find this value in the row corresponding to 14 degrees of freedom in the t-distribution table.

3. Identify the P-value: The P-value is the probability that you would observe a test statistic as extreme as t0, assuming the null hypothesis is true. In the t-distribution table, you will find a range of P-values corresponding to t0 = 0.89. This range is your answer.

Please note that the exact P-value depends on the specific t-distribution table you are using, as different tables may have slightly different values. Also, since this is a one-tailed test (𝐻1:𝜇>100), you should look at the one-tailed P-values in the table.