Question

HW principle provides null hypothesis about

Answer 1

The null hypothesis in hypothesis testing states that there is no difference or relationship between the groups being compared or the variables being tested.

Step-by-step explanation:

The null hypothesis in hypothesis testing provides the assumption that there is no difference between the groups being compared or no relationship between variables. For example, in a hypothesis test comparing two sample means, the null hypothesis would state that the means are equal. In a test of independence, the null hypothesis would state that the factors being tested are independent. By providing a null hypothesis, hypothesis testing allows us to evaluate if there is enough evidence to reject this assumption in favor of an alternative hypothesis that suggests a difference or relationship exists.

Answer 2

The HW principle, also known as Neyman-Pearson null hypothesis testing, is a statistical method that begins with a null hypothesis suggesting no differences or effects exist among experimental units. If data does not support the null hypothesis, alternative hypotheses are considered. This principle is a foundational concept in classical hypothesis testing procedures in statistics.

Step-by-step explanation:

The HW principle, or the Neyman-Pearson null hypothesis testing (NHT), provides a null hypothesis about the assumption that there are no differences between experimental units. According to the principle, the null hypothesis is essentially a statement that posits no observable effect or no difference exists — for example, no difference between sample means or proportions, or between a sample and population mean or proportion. The idea is that if the data does not support the null hypothesis, it suggests the possibility of alternative hypotheses, which claim that differences do exist, although these are not explicitly evaluated within the scope of NHT.

The null hypothesis often states that all groups or samples come from populations with the same normal distribution and have equal variances. A traditional approach in verification is to set a threshold for Type I errors, also known as α errors, which represent the probability of incorrectly rejecting a true null hypothesis. Should the data not conform to this null model, one may declare a 'significant' effect, hence potentially validating an alternative hypothesis.