When making decisions based on tests of hypothesis, two types of errors can occur:

1. Type I Error (False Positive): This occurs when we reject the null hypothesis even though it is true. In other words, we are seeing a difference when in fact there is none. The probability of making a Type I error is denoted by alpha (α), which is the level of significance you set for your hypothesis test.

2. Type II Error (False Negative): This occurs when we fail to reject the null hypothesis when it is actually false. In other words, we are not seeing a difference when in fact there is one. The probability of making a Type II error is denoted by beta (β), and 1-β is called the power of the test.

The likelihood of these errors occurring can be influenced by several factors, including the sample size, the variance in the data, and the significance level chosen for the test.

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When making decisions based on tests of hypothesis, two types of errors can occur:

1. Type I Error (False Positive): This occurs when we reject the null hypothesis even though it is true. In other words, we are seeing a difference when in fact there is none. The probability of making a Type I error is denoted by alpha (α), which is the level of significance you set for your hypothesis test.

2. Type II Error (False Negative): This occurs when we fail to reject the null hypothesis when it is actually false. In other words, we are not seeing a difference when in fact there is one. The probability of making a Type II error is denoted by beta (β), and 1-β is called the power of the test.

The likelihood of these errors occurring can be influenced by several factors, including the sample size, the variance in the data, and the significance level chosen for the test.

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When making decisions based on tests of hypothesis, two types of errors can occur:

1. Type I Error (False Positive): This occurs when we reject the null hypothesis even though it is true. In other words, we are seeing a difference when in fact there is none. The probability of making a Type I error is denoted by alpha (α), which is the level of significance you set for your hypothesis test.

2. Type II Error (False Negative): This occurs when we fail to reject the null hypothesis when it is actually false. In other words, we are not seeing a difference when in fact there is one. The probability of making a Type II error is denoted by beta (β), and 1-β is called the power of the test.

The likelihood of these errors occurring can be influenced by several factors, including the sample size, the variance in the data, and the significance level chosen for the test.

What types of error can occur when making decisions based on test of hypothesis? Be specific.

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