# Basic Quantitative Analysis - Essay Example

Summary
Distinguish between the null hypothesis and the research hypothesis. When does the researcher decide to reject the null hypothesis? The null hypothesis is something that the researcher is actually trying to disprove or reject. A researcher rejects the null hypothesis when…

## Extract of sample "Basic Quantitative Analysis"

Obtained results are the results that the test demonstrates overall. The factors that are most important when determining whether these results will be satisfied are the hypothesis, the research itself, and the statistical significance of the research.
Type I and Type II errors are both types of errors that can be made in significance testing. A Type I error occurs when a null hypothesis is rejected, and it should not have been rejected. A Type II error occurs when a false null hypothesis is not rejected. The probability of a Type I error is designated by the Greek letter alpha (a) and is called the Type I error rate; the probability of a Type II error (the Type II error rate) is designated by the Greek letter beta (ß) . The false positive rate can be defined, according to Type I and Type II Errors (n.d.) as:
the proportion of negative instances that were erroneously reported as being positive. It is equal to 1 minus the specificity of the test. This is equivalent to saying the false positive rate is equal to the significance level. This means the false positive rate = number of false positives/total number of negative instances.
According to “Type I and Type II Errors” (n.d.): “The false negative rate is the proportion of positive instances that were erroneously reported as negative. It is equal to 1 minus the power of the test. False negative rate = number of false negatives/total number of positive instances. Type II errors can be caused by a lack of sensitivity or, In many cases, an oversight.
Statistical significance means that the noticed mean variations are probably not due to a sampling error. Even a small sample, if it is large enough for the test, can work for statistical significance. Practical significance, on the other hand, considers if the difference is adequate enough to be of help in a practical sense.
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