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Heuer outlines the ACH process in considerable depth in his book, Psychology of Intelligence Analysis. [1] It consists of the following steps: Hypothesis – The first step of the process is to identify all potential hypotheses, preferably using a group of analysts with different perspectives to brainstorm the possibilities.
Testing a hypothesis suggested by the data can very easily result in false positives (type I errors). If one looks long enough and in enough different places, eventually data can be found to support any hypothesis. Yet, these positive data do not by themselves constitute evidence that the hypothesis is correct. The negative test data that were ...
To illustrate, consider an example from Cook et al. where the analysis task is to find the variables which best predict the tip that a dining party will give to the waiter. [12] The variables available in the data collected for this task are: the tip amount, total bill, payer gender, smoking/non-smoking section, time of day, day of the week ...
An example of Neyman–Pearson hypothesis testing (or null hypothesis statistical significance testing) can be made by a change to the radioactive suitcase example. If the "suitcase" is actually a shielded container for the transportation of radioactive material, then a test might be used to select among three hypotheses: no radioactive source ...
The hypothetico-deductive model or method is a proposed description of the scientific method.According to it, scientific inquiry proceeds by formulating a hypothesis in a form that can be falsifiable, using a test on observable data where the outcome is not yet known.
Naaman [3] proposed an adaption of the significance level to the sample size in order to control false positives: α n, such that α n = n − r with r > 1/2. At least in the numerical example, taking r = 1/2, results in a significance level of 0.00318, so the frequentist would not reject the null hypothesis, which is in agreement with the ...
The statement that is being tested against the null hypothesis is the alternative hypothesis. [2] Alternative hypothesis is often denoted as H a or H 1 . In statistical hypothesis testing , to prove the alternative hypothesis is true, it should be shown that the data is contradictory to the null hypothesis.
[68] [69] For example, the hypothesis might be that "Unemployment has no effect on inflation", which relates to an economics concept called the Phillips Curve. [70] Hypothesis testing involves considering the likelihood of Type I and type II errors , which relate to whether the data supports accepting or rejecting the hypothesis.