It is also called a False Positive condition (a situation which indicates that a given condition is not present but it actually is present).
But if using experimental data, we do not detect an effect of floride added on cavities then we are accepting a false null hypothesis. Floride added to a toothpaste has effect against cavities. Generally It is acceptable to have Type I error significance level as 0.05 or 5% which means that 5% probability of incorrectly rejecting the null hypothesis is acceptable. Type I error is denoted by $ \alpha $ and is also called alpha level. The Type I error rate or significance level of Type I is represented by the probability of rejecting the null hypothesis given that it is true. It is also called a False Positive condition (a situation which indicates that a given condition is present but it actually is not present). But if using experimental data, we detect an effect of water added on cavities then we are rejecting a true null hypothesis. Water added to a toothpaste has no effect against cavities. Here Null hypothesis is to be tested against experimental data to nullify the effect of floride and water on teeth's cavities. Null Hypothesis - Floride added to a toothpaste has no effect against cavities. Hypothesis - Floride added to a toothpaste protects teeth against cavities. Null Hypothesis - Water added to a toothpaste has no effect against cavities. Hypothesis - Water added to a toothpaste protects teeth against cavities. Consider the following examples: Example 1 Null Hypothesis refers to a statement which nullifies the contrary with evidence. Type I error represents the incorrect rejection of a valid null hypothesis whereas Type II error represents the incorrect retention of an invalid null hypothesis. Type I and Type II errors signifies the erroneous outcomes of statistical hypothesis tests. Regression Intercept Confidence Interval.Process Capability (Cp) & Process Performance (Pp).Data collection - Questionaire Designing.
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