Decision Rule and Power of a Test | CFA Level 1 - AnalystPrep (2024)

FAQs

Decision Rule and Power of a Test | CFA Level 1 - AnalystPrep? ›

The decision rule refers to the procedure followed by analysts and researchers when deciding whether to reject or not to reject a null hypothesis. We use the phrase “not to reject” because it is considered statistically incorrect to “accept” a null hypothesis. Rather, we can only assemble enough evidence to support it.

P-Value in Your CFA Exam

The p-value is the smallest level of significance at which the null hypothesis can be rejected. Remember, if the p-value is higher than \alpha, we have no reason to reject the null hypothesis and the test is not statistically significant.

The decision rule, "reject if |t| > critical value associated with α" is equivalent to "reject if p < α." SAS will provide the p-value, the probability that T is more extreme than observed t. The decision rule, "reject if |t| > critical value associated with α" is equivalent to "reject if p < α."

Decision rule is an element (piece) of knowledge, usually in the form of “if-then statement”: if then . If its Condition is satisfied (i.e., matches a fact in the corresponding database of a given problem), then its Action (e.g., classification or decision making) is performed.

The p-value is the lowest level of significance at which we can reject a null hypothesis. The probability of coming up with a test statistic would justify our rejection of a null hypothesis, assuming that the null hypothesis is indeed true.

The Chartered Financial Analyst credential is one of the most demanding exams on Earth when it comes to preparation and study time required. The average pass rate for the CFA Level 1 is only 41%. For Level 2, you're looking at a passage rate of 45%. And Level 3 is not much easier at 52%.

The score needed to pass the exam is approximately 70%. I observed that students who scored near 70 in all subjects passed. There were some cases where a person missed 70 in one subject but still passed. The score required for the 90th percentile is around 85%.

The decision rule is based on specific values of the test statistic (e.g., reject H₀ if Z > 1.645). The decision rule for a specific test depends on 3 factors: the research or alternative hypothesis, the test statistic and the level of significance.

A decision rule is a simple IF-THEN statement consisting of a condition (also called antecedent) and a prediction. For example: IF it rains today AND if it is April (condition), THEN it will rain tomorrow (prediction). A single decision rule or a combination of several rules can be used to make predictions.

A p-value less than 0.05 is typically considered to be statistically significant, in which case the null hypothesis should be rejected. A p-value greater than 0.05 means that deviation from the null hypothesis is not statistically significant, and the null hypothesis is not rejected.

What is the most common decision rule? ›

Simple majority rule means that an agreement is validated when the majority (over 50%) of voting parties support it.

In decision theory, a decision rule is a function which maps an observation to an appropriate action. Decision rules play an important role in the theory of statistics and economics, and are closely related to the concept of a strategy in game theory.

Typically ranging between 0 and 1, values below 0.3 suggest weak influence, while those between 0.3 and 0.5 indicate moderate influence. Values exceeding 0.7 signify a strong effect on the dependent variable.

These numbers can give a false sense of security. Most authors refer to statistically significant as P < 0.05 and statistically highly significant as P < 0.001 (less than one in a thousand chance of being wrong).

He proposed “if P is between 0.1 and 0.9 there is certainly no reason to suspect the hypothesis tested. If it's below 0.02 it is strongly indicated that the hypothesis fails to account for the whole of the facts.

The threshold value, P < 0.05 is arbitrary. As has been said earlier, it was the practice of Fisher to assign P the value of 0.05 as a measure of evidence against null effect. One can make the “significant test” more stringent by moving to 0.01 (1%) or less stringent moving the borderline to 0.10 (10%).

Level 1 CFA Exam: Definition of Profitability Index

The profitability index (PI) is the ratio of the present value of future cash inflows to the initial investment. If a project has a PI greater than 1, you should invest in the project. If the PI is lower than 1, then the project is not profitable.

A p-value less than 0.05 is typically considered to be statistically significant, in which case the null hypothesis should be rejected. A p-value greater than 0.05 means that deviation from the null hypothesis is not statistically significant, and the null hypothesis is not rejected.

Some years test takers will receive a 65% overall score and fail, while in other years candidates have received a 62% score and passed. How the scoring is weighted plays heavily on how your overall score will turn out. Generally speaking, any score of 70% or higher should be a passing score.