How do you know if t test is significant or not?

A result is considered to be statistically significant when its p-value is lower than a set value deemed “acceptable” for type I error, which is generally 0.05 (a 5% chance of error, i.e., of concluding that the difference found is significant when it actually reflects chance alone).
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How to know if a t-test is significant?

If a p-value reported from a t test is less than 0.05, then that result is said to be statistically significant.
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What makes a T-value significant?

Higher values of the t-score indicate that a large difference exists between the two sample sets. The smaller the t-value, the more similarity exists between the two sample sets. A large t-score, or t-value, indicates that the groups are different while a small t-score indicates that the groups are similar.
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How do you interpret the results of the t-test?

Interpret t-value

Regardless of which t-test we calculate, the t-value becomes larger the greater the difference between the means. In the same way, the t-value becomes smaller when the difference between the means is smaller. Also, the t-value becomes smaller if we have a larger dispersion of the mean values.
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How do you know if it is significant or not significant?

The level at which one can accept whether an event is statistically significant is known as the significance level. Researchers use a measurement known as the p-value to determine statistical significance; if the p-value falls below the significance level, then the result is statistically significant.
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Statistical Significance, the Null Hypothesis and P-Values Defined & Explained in One Minute

Is 0.05 significant or not significant?

If the p-value is less than 0.05, it is judged as “significant,” and if the p-value is greater than 0.05, it is judged as “not significant.” However, since the significance probability is a value set by the researcher according to the circumstances of each study, it does not necessarily have to be 0.05.
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What is the p-value in the t-test?

In a statistical test (such as a t-test which you can find explained here), the p-value lets you determine the probability that you can disprove the null hypothesis. The null hypothesis is the one you are trying to disprove. If you can reject the null hypothesis, you can accept the alternative hypothesis.
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How do you interpret t-score and p-value?

A big t, with a small p-value, means that the null hypothesis is discredited, and we would assert that the means are significantly different in the way specified by the null hypothesis (and a small t, with a big p-value means they are not significantly different in the way specified by the null hypothesis).
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What is the significance of the t-test value?

T test formula

A larger t value shows that the difference between group means is greater than the pooled standard error, indicating a more significant difference between the groups.
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What p-value is significant?

A p-value less than 0.05 is typically considered to be statistically significant, in which case the null hypothesis should be rejected. A p-value greater than 0.05 means that deviation from the null hypothesis is not statistically significant, and the null hypothesis is not rejected.
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Is the t-value significant at the 0.05 level?

Understanding t-Tests and Critical Values

A significance level of (for example) 0.05 indicates that in order to reject the null hypothesis, the t-value must be in the portion of the t-distribution that contains only 5% of the probability mass.
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What is the value of significance in t-test?

The test provides a p-value, which is the probability of observing results as extreme as those in the data assuming the results are truly due to chance alone. A p-value of 5% or lower is often considered to be statistically significant.
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What is the null hypothesis for the t-test?

The Independent-Samples t Test

The null hypothesis is that the means of the two populations are the same: µ1 = µ2. The alternative hypothesis is that they are not the same: µ1 ≠ µ2. Again, the test can be one-tailed if the researcher has good reason to expect the difference goes in a particular direction.
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What is a good T value?

Generally, a t-statistic of 2 or higher is considered to be statistically significant.
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How do you check for t-test conditions?

t-Test assumptions
  1. The data are continuous.
  2. The sample data have been randomly sampled from a population.
  3. There is homogeneity of variance (i.e., the variability of the data in each group is similar).
  4. The distribution is approximately normal.
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How do you find the t-score from significance level?

Step 1: Express the confidence level as a number (decimal) with 0 < c < 1 . Step 2: Obtain the significance level, denoted , by α = 1 − c . Step 3: Use the -table or a calculator to obtain the -score (critical value) t α / 2 where (i) the is from Step 2 and (ii) the degrees of freedom equals , where is the sample size.
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How do you know if your t-test value is significant?

If a p-value reported from a t test is less than 0.05, then that result is said to be statistically significant.
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How to interpret a t-test?

Once you've conducted a t-test, you'll obtain a t-statistic and a corresponding p-value. Here's how to interpret the results: T-Statistic: The t-statistic measures the size of the difference relative to the variation in your sample data. A larger t-statistic indicates a greater difference between the group means.
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How to write t-test results?

When reporting the result of an independent t-test, you need to include the t-statistic value, the degrees of freedom (df) and the significance value of the test (p-value). The format of the test result is: t(df) = t-statistic, p = significance value.
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What is the significance level of the T value?

The t table can be used for both one-sided (lower and upper) and two-sided tests using the appropriate value of α. The significance level, α, is demonstrated in the graph below, which displays a t distribution with 10 degrees of freedom. The most commonly used significance level is α = 0.05.
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What is the critical value of t?

A critical value of t defines the threshold for significance for certain statistical tests and the upper and lower bounds of confidence intervals for certain estimates. It is most commonly used when: Testing whether two means are significantly different (two-sample t tests)
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How to interpret t-test results in SPSS?

To interpret the t-test results, all you need to find on the output is the p-value for the test. To do an hypothesis test at a specific alpha (significance) level, just compare the p-value on the output (labeled as a “Sig.” value on the SPSS output) to the chosen alpha level.
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How do you interpret the t-score?

For negatively-worded concepts like fatigue, a higher T-score represents greater fatigue and a lower T-score represents less fatigue. For positively-worded concepts like physical function, a higher T-score reflects higher (better) physical function and a lower T-score reflects lower (worse) physical function.
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What does p-value 0.04 mean in t-test?

In this context, what P = 0.04 (i.e., 4%) means is that if the null hypothesis is true and if you perform the study a large number of times and in exactly the same manner, drawing random samples from the population on each occasion, then, on 4% of occasions, you would get the same or greater difference between groups ...
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What does p 0.01 mean in t-test?

If you have some hypothesis, and you get a p-value of 0.01, it means that given your hypothesis, there is a 1% chance of observing a result at least as extreme as what you observed.
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