Q: How do I interpret t-test results?

Look at the p-value, confidence interval, and effect size. A p-value <0.05 typically indicates a significant difference. The confidence interval shows the range of plausible differences.

Q: What is effect size in a t-test?

Effect size (e.g., Cohen's d) measures the magnitude of the difference between group means, beyond whether the difference is statistically significant. It helps understand the practical importance of the difference.

Q: When should I use a Z-test instead of a t-test?

A Z-test is used when you know the population variance and have a large sample size. A t-test is more appropriate when the population variance is unknown and estimated from the sample, especially for small samples.

Q: What is the null hypothesis in a t-test?

The null hypothesis (H0) in a t-test typically states that there is no significant difference between the means of the two groups being compared. The t-test assesses whether there is enough evidence to reject this hypothesis.

Question 1

What is a two-sample t-test and when should I use it?

Accepted Answer

A two-sample t-test compares the means of two independent groups to determine if they are significantly different. Use it when comparing means between two groups with continuous data.

Question 2

What is the difference between paired and unpaired t-tests?

Accepted Answer

Paired t-tests compare related measurements (before/after, matched pairs). Unpaired t-tests compare independent groups. Choose based on your study design and data structure.

Question 3

How do I know if my data meets the t-test assumptions?

Accepted Answer

T-tests assume: normal distribution (or large sample size), independent observations, and equal variances between groups. The tool may provide tests for these assumptions.

Question 4

What sample size do I need for reliable results?

Accepted Answer

For small samples (usually n < 30), it is important that the data in each group be approximately normally distributed or that there are no significant outliers. As sample size increases, the impact of deviations from normality is reduced by the central limit theorem.

Question 5

How do I interpret t-test results?

Accepted Answer

Look at the p-value, confidence interval, and effect size. A p-value <0.05 typically indicates a significant difference. The confidence interval shows the range of plausible differences.

Question 6

What is effect size in a t-test?

Accepted Answer

Effect size (e.g., Cohen's d) measures the magnitude of the difference between group means, beyond whether the difference is statistically significant. It helps understand the practical importance of the difference.

Question 7

When should I use a Z-test instead of a t-test?

Accepted Answer

A Z-test is used when you know the population variance and have a large sample size. A t-test is more appropriate when the population variance is unknown and estimated from the sample, especially for small samples.

Question 8

What is the null hypothesis in a t-test?

Accepted Answer

The null hypothesis (H0) in a t-test typically states that there is no significant difference between the means of the two groups being compared. The t-test assesses whether there is enough evidence to reject this hypothesis.

A/B testing Calculator: Two Sample T-test

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Features of the "Two-Sample T-Test"

Compare Mean Values of Two Samples

Useful for Analytics and Scientific Research

Ease of Result Interpretation

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Sample Size Calculator

Chi-Square Test

Sequential Sampling Calculator

A/B testing Calculator: Two Sample T-test

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