Test statistic

Definition

A test statistic is a standardized value that is calculated from sample data during a hypothesis test. It helps determine whether to reject the null hypothesis by comparing the observed data against a known distribution under the null hypothesis. The value of the test statistic indicates how far the sample statistic deviates from the null hypothesis, allowing researchers to assess the strength of the evidence against it.

5 Must Know Facts For Your Next Test

1. The test statistic can take different forms depending on the type of test being performed, such as t-tests, z-tests, or chi-squared tests.
2. The larger the absolute value of the test statistic, the stronger the evidence against the null hypothesis, suggesting that it should be rejected.
3. Test statistics are calculated using sample data, which means they are subject to variability and should be interpreted within the context of sampling distributions.
4. Common test statistics include t-statistics for comparing means and z-statistics for proportion tests, each having their own critical values for decision-making.
5. Test statistics are crucial in determining the p-value, which helps in making decisions about hypotheses based on statistical significance levels.

Review Questions

• How does a test statistic relate to the decision-making process in hypothesis testing?
  • The test statistic plays a central role in hypothesis testing by providing a numerical measure that quantifies how much sample data deviates from what is expected under the null hypothesis. Based on its value, researchers can compare it to critical values or use it to calculate a p-value. This helps decide whether there is sufficient evidence to reject the null hypothesis in favor of an alternative hypothesis.
• What factors influence the value of a test statistic, and how can these factors impact hypothesis testing outcomes?
  • The value of a test statistic is influenced by sample size, variability in data, and the difference between sample statistics and hypothesized population parameters. A larger sample size generally provides a more reliable estimate of the population parameters, leading to a more accurate test statistic. If variability is high, it may lead to a smaller test statistic and a less conclusive result, potentially impacting whether we reject or fail to reject the null hypothesis.
• Evaluate how different types of test statistics (e.g., t-statistic vs. z-statistic) affect conclusions drawn from hypothesis tests.
  • Different types of test statistics, like t-statistics and z-statistics, serve specific purposes based on sample characteristics and population knowledge. The t-statistic is used when sample sizes are small or when population standard deviation is unknown, offering more accurate results in those cases. In contrast, z-statistics apply to larger samples with known population standard deviations. Choosing the appropriate test statistic impacts conclusions drawn from hypothesis tests, as using one over another can change whether we reject or fail to reject the null hypothesis based on its calculated value.

Related terms

null hypothesis:

A statement asserting that there is no effect or no difference, used as a starting point for statistical testing.

p-value:

The probability of observing a test statistic as extreme as, or more extreme than, the value observed, assuming that the null hypothesis is true.

critical value: A threshold value that defines the boundary for rejecting the null hypothesis in a statistical test.