Definitions

In this post I define terms used in the lectures and textbook in the discussion of hypothesis testing.

Hypothesis Testing: a decision-making process for evaluating claims about a population.

Null Hypothesis (H_{0}): a conjecture that states that a population parameter is equal to a certain value. The value chosen is usually based on historical data or some other reliable source. The null hypothesis may also state that a population parameter is greater than or equal to or less than or equal to a certain value. In any case, the null hypothesis always contains an equality.

Alternative Hypothesis (H_{1} or H_{A}): a conjecture that states that a population parameter is not equal to a certain value. The alternative hypothesis is the complement of the null hypothesis.

Examples:

H_{0}: μ = 56

H_{1}: μ ≠: 56

H_{0}: μ ≤ 4.5

H_{1}: μ >: 4.5

H_{0}: μ ≥ $102

H_{1}: μ <: $102

Note: In these examples, I use the parameter μ, the population mean, because we looked at hypothesis testing of the mean in the lecture. However, there are methods for doing hypothesis testing for the proportion (section 9.5 in our text) as well as other parameters which we did not cover.

Rejection Region: An area of the sampling distribution. If the test statistic falls into the rejection region, we reject the null hypothesis in favor of the alternative hypothesis.

Non-rejection region: An area of the sampling distribution which is the complement of the rejection region. If the test statistic falls in the non-rejection region, we say that we do not have evidence to reject the null hypothesis.

Critical Value: The value which divides the rejection region from the non-rejection region.

Type I Error: An error of rejecting the null hypothesis, H_{0}, when it is true.

Type II Error: An error of not rejecting the null hypothesis, H_{0}, when it is false.

Level of Significance, α: The probability of committing a Type I error in a statistical test. Typically, the level of significance is controlled by specifying this value before the test is conducted and determining the rejection region based on it. A lower level of significance (lower probability of Type I error) requires a smaller rejection region because we are more cautious not to reject H_{0}. Typical values for α are 0.01, 0.05 and 0.10.

Confidence Coefficient: The complement of the level of significance: 1 - α.

Confidence Level: The confidence coefficient expressed as a percentage: (1 - α) x 100.

β (Beta) Risk: The probability of committing a Type II error in a statistical test. The value of the β risk is difficult to determine. Among other factors, it depends on the difference between the hypothesized value of the parameter being tested and the actual value of the parameter. If we knew that difference, we wouldn't need to do any testing!

Power of a Test: The complement of the β risk: 1 - β.

Two-Tail Test: A statistical test in which the null hypothesis, H_{0}, is that a population parameter is strictly equal to a specific value. In such a case, the rejection region is divided into two halves (i.e. two tails) on either side of the sampling distribution of the test statistic.

One-Tail Test: A statistical test in which the null hypothesis, H_{0}, is either greater than or equal to or less than or equal to a specific value. In such a case, the rejection region is entirely on one half (i.e. one tail) of the sampling distribution of the test statistic.

## Sunday, February 24, 2008

### Hypothesis Testing - Definitions

Subscribe to:
Post Comments (Atom)

## No comments:

Post a Comment