The ericsson method is one of several methods claimed to increase the likelihood of a baby girl. in a clinical trial, results could be analyzed with a formal hypothesis test with the alternative hypothesis of pgreater than> 0.5, which corresponds to the claim that the method increases the likelihood of having a girl, so that the proportion of girls is > 0.5. if you have an interest in establishing the success of the method, which of the following p-values would you prefer: 0.999, 0.5, 0.95, 0.05, 0.01, 0.001? why?

The preferred p-value among the options offered is 0.001. The reason is because p-value represents the minimum probability of committing the type I error, that is, the probability of rejecting the null hypothesis, with the information contained in the sample, given that this hypothesis is true. The p-value of 0.001 is the lowest value proposed for this probability. Therefore, it supports more evidence that the null hypothesis is false.

Step-by-step explanation:

The preferred P-value among the options provided is 0.001.

0.001 is the preferred p-value simply because it shows the minimum probability of committing an error, it also implies that the probability of rejecting the null hypothesis based on the information presented in the sample, considering that the hypothesis is true.  

Therefore, the p-value 0.001 is the lowest value proposed for this probability and it also corresponds to the sample of evidence that supports the alternative hypothesis which shows the method is effective.

Hypothesis testing refers to an act in statistics in which an analyst test the assumption as regards to a population parameter.

The methodology to be used by analysts is based on the nature of the data that are used and the purpose of the analysis

An analysts test a sample to accept or reject a null hypothesis. The outcome of the tests will reveal the analysis if his primary hypothesis is true or not. If the analysis of the tests is not true, it then means the analysts will have to formulate a new hypothesis and do the analysis again.

It implies the analysts will continue to repeat the process until the analysis shows the data hypothesis is true.  

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step-by-step explanation:

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