Below you will find complete descriptions and links to 14 different analytics calculators for computing the t-tests and t-values.

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Compute the t-value and degrees of freedom for a Pearson correlation coefficient, given the sample size and the value of the correlation coefficient r. Knowing the t-value and degrees of freedom that are associated with a particular correlation coefficient can be useful when comparing correlations or when seeking to conduct additional analyses in an analytics study.

Compute the cumulative distribution function (CDF) for the noncentral t-distribution, given a t-value, the value of the noncentrality parameter, and the degrees of freedom. The noncentral t-distribution CDF yields the area under the noncentral t-distribution from negative infinity to t, which is very useful for analytics-related tasks such as computing statistical power and robust data modeling.

Compute the probability density function (PDF) for the noncentral t-distribution, given the degrees of freedom, the value of the noncentrality parameter, and a t-value. The noncentral t-distribution PDF identifies the relative likelihood that an associated random variable will have a particular value, and is very useful for analytics studies that incorporate noncentral t-distribution probabilities.

Compute a complete one-sample t-test, given the sample size, the observed the sample mean, the hypothesized mean, and the sample standard deviation. The calculator computes the t-value, the degrees of freedom, the critical t-value and p-value for a one-tailed (directional) hypothesis, and the critical t-value and p-value for a two-tailed (non-directional) hypothesis. Conducting one-sample t-tests is very common in a wide variety of analytics studies.

Compute the 90%, 95%, and 99% confidence intervals for the mean of a normal population, given the sample standard deviation, the sample mean, and the sample size. Knowing the confidence interval for the mean of a normal population can be very useful for assessing the true nature of a population variable in analytics studies that use normally distributed sample data.

Compute the cumulative distribution function (CDF) for the t-distribution, given a t-value and the degrees of freedom. The t-distribution CDF yields the area under the t-distribution from negative infinity to t, which is very useful for assessing probabilities in analytics studies that rely on t-tests.

Compute the probability density function (PDF) for the t-distribution, given a t-value and the degrees of freedom. The t-distribution PDF is very useful for identifying critical values and assessing probabilities in analytics studies that rely on t-tests.

Compute the two-tailed Cohen's d effect size for a t-test, given the mean and standard deviation for two independent samples of equal size. Knowing the effect size is often very useful when comparing or reporting the results of analytics studies that rely on t-tests.

Compute the observed power for a one-tailed or two-tailed t-test study, given the observed p-value, the observed effect size, and the total sample size. When a t-based model is not significant in an analytics study, it may be useful to know whether the model had sufficient power to detect the expected or hypothesized effect.

Compute the one-tailed and two-tailed probability values for a t-test, given the t-value and the degrees of freedom. Knowing the probability values for a t-test is often very useful in analytics when making decisions about hypotheses or the usefulness of a predictor variable.

Compute the minimum required total sample size and per-group sample size for a one-tailed or two-tailed t-test study, given the p-value, the expected effect size, and the statistical power level. Knowing if your sample is large enough to detect an expected or hypothesized effect is critical when conducting analytics studies that rely on t-tests.

Compute the Type 2 error rate (false negative rate) for a one-tailed or two-tailed t-test study, given the observed p-value, the observed effect size, and the total sample size. Knowing the chances of observing a false negative when using a t-test is often very important in analytics.

Compute the t-value and degrees of freedom for a t-test, given the sample size, the observed sample mean, the hypothesized mean, and the sample standard deviation. Knowing the t-value and degrees of freedom can be very useful in analytics studies that rely on t-tests.

Compute the one-tailed (right-tail) and two-tailed t-values associated with a given probability level and degrees of freedom. Knowing the t-value for a particular probability and degrees of freedom is often useful in analytics studies when considering hypotheses, comparing predictor variables, or reporting results.