Every t-value has a p-value, explain.
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Explain why every t-value has a p-value
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When you perform a t-test, you’re usually trying to find evidence of a significant difference between population means (2-sample t) or between the population mean and a hypothesized value (1-sample t). The t-value measures the size of the difference relative to the variation in your sample data. Put another way, T is simply the calculated difference represented in units of standard error. The greater the magnitude of T, the greater the evidence against the null hypothesis.The larger the absolute value of the t-value, the smaller the p-value, and the greater the evidence against the null hypothesis
T-Value describes how closely related your data is with the null hypothesis. P-value is the measure of how true is the Null Hypothesis. Lower the P-value, the more significant is the relationship between the independent and dependent variable
t-Test is conducted to determine the difference between two distributions and the measure of this difference is given by the t-score or t-value. A large t-value states that the distributions are different and a small t-value indicates that the distributions are similar. Now this test is conducted by taking samples from the population and there can be cases that the result of t-test occurred by chance alone. So to add a statistical significance to this t-value we can do the Hypothesis testing and thus comes in picture the p-value. This hypothesis test is conducted by assuming two alternating hypothesis.
null hypothesis: both the sample distributions are same.
Alternate hypothesis: both the sample distributions are different.
p-value indicates the probability of observing the test results if null hypothesis is true.
Lower the p-value, better it is as the null hypothesis can be rejected.
Higher the p-value, more the probability that the results occurred by chance alone and hence accepting null hypothesis.
Thus every t-value has p-value attached to it for providing the statistical significance to it.
T value is the measure which used to confirm the null hypothesis can be rejected or accepted. P value tells about the probability how much null hypothesis is true. T value has p value inorder to confirm or not confirm the null hypothesis. In t distribution, t value represent the extreme value from where the alternate hypothesis is true. The area under the curve from t value is the p value which confirm the alternative hypothesis.
t test is calculated to measure the significant difference between population and sample mean. And p value measured the acceptance of level of error. Higher value of t is lower value of p,which results in rejecting the null hypothesis.
Yes, every t-value has a p-value. The p-value is calculated from the t-value itself. It is basically the least probability beyond which if the level of significance goes, the possibility of rejecting the null hypothesis increases. The p-value is calculated under the assumption that the null hypothesis is true but then the actual value helps us understand how much in favor of the null hypothesis it actually is and how much just by chance.
Yes, every t value has the p-value.
t-value means the test statistics which follows the student t distribution where as p-value is the probability of observing as extreme as test statistics assuming null hypothesis is true.
This means p-value will tells the probability of observing the result as extreme as t-value.
T values of larger magnitudes (either negative or positive) are less likely. The far left and right “tails” of the distribution curve represent instances of obtaining extreme values of t, far from 0. For example, the shaded region represents the probability of obtaining a t-value of 2.8 or greater. Imagine a magical dart that could be thrown to land randomly anywhere under the distribution curve. What’s the chance it would land in the shaded region? The calculated probability is 0.005712…..which rounds to 0.006…which is…the p-value obtained in the t-test results.
The larger the absolute value of the t-value, the smaller the p-value, and the greater the evidence against the null hypothesis.
p value: helps us to strengthen the results
t value: we use when the population is unknown
suppose we have t value=2.75
-We will check what is the probability of t value above the mean
-what is the the probability of t value below the mean
-we will check the 2.75 value in t critical values is 0.02
-The p value above the mean and p value below the mean is 0.04.
-if p value is greater than null hypothesis we will accept otherwise we reject