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 assume that there is a significant effect or relationship when in reality, there isn't. The probability of making a Type I error is denoted by the Greek letter alpha (α), which is also known as the significance level. For example, if α = 0.05, it means there is a 5% risk of concluding that a difference exists when there is no actual difference.

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 assume that there is no significant effect or relationship when in reality, there is. The probability of making a Type II error is denoted by the Greek letter beta (β). The power of a test (1 - β) is the probability that it correctly rejects a false null hypothesis.

The likelihood of these errors occurring can be influenced by several factors, including the significance level chosen, the sample size, and the effect size.

Question

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 assume that there is a significant effect or relationship when in reality, there isn't. The probability of making a Type I error is denoted by the Greek letter alpha (α), which is also known as the significance level. For example, if α = 0.05, it means there is a 5% risk of concluding that a difference exists when there is no actual difference.

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 assume that there is no significant effect or relationship when in reality, there is. The probability of making a Type II error is denoted by the Greek letter beta (β). The power of a test (1 - β) is the probability that it correctly rejects a false null hypothesis.

The likelihood of these errors occurring can be influenced by several factors, including the significance level chosen, the sample size, and the effect size.

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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 assume that there is a significant effect or relationship when in reality, there isn't. The probability of making a Type I error is denoted by the Greek letter alpha (α), which is also known as the significance level. For example, if α = 0.05, it means there is a 5% risk of concluding that a difference exists when there is no actual difference.

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 assume that there is no significant effect or relationship when in reality, there is. The probability of making a Type II error is denoted by the Greek letter beta (β). The power of a test (1 - β) is the probability that it correctly rejects a false null hypothesis.

The likelihood of these errors occurring can be influenced by several factors, including the significance level chosen, the sample size, and the effect size.

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

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

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