When we build anything, we ask the question: Did it turn out as intended? Or more simply, did it meet spec? Whether it’s the diameter of a piston, the weight of a bolt of cloth, the percentage of sugar in a soft drink or the length of a straw, there are always two outcomes: Yes or no! But asking that question has four possible answers. 1. Yes, it meets spec (and it really does); 2. No, it doesn’t meet spec (and it really doesn’t); 3. Yes, we say it meets spec (but it doesn’t); and 4. No, we reject the product (when it’s really okay).
Science calls these Type 1 and Type 2 errors.
If I reject the belief and scrap the part, when it is actually good, I’ve committed a Type 1 error.
SELLERS RISK! Bad for the seller – Type 1 Error
If I accept the belief and ship the part when it is actually bad, I’ve committed a Type II error.
BUYERS RISK! Bad for the buyer – Type 2 Error
Data collection is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a component of research in all fields of study including physical and social sciences, humanities, and business. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. The goal for all data collection is to capture quality evidence that allows analysis to lead to the formulation of convincing and credible answers to the questions that have been posed.how to we assess
A perfect test would have zero false positives and zero false negatives. However, statistics is a game of probability, and it cannot be known for certain whether statistical conclusions are correct. Whenever there is uncertainty, there is the possibility of making an error. Considering this nature of statistics science, all statistical hypothesis tests have a probability of making type I and type II errors.
• The type I error rate or significance level is the probability of rejecting the null hypothesis given that it is true. It is denoted by the Greek letter α (alpha) and is also called the alpha level. Usually, the significance level is set to 0.05 (5%), implying that it is acceptable to have a 5% probability of incorrectly rejecting the true null hypothesis.
• The rate of the type II error is denoted by the Greek letter β (beta) and related to the power of a test, which equals 1−β.
These two types of error rates are traded off against each other: for any given sample set, the effort to reduce one type of error generally results in increasing the other type of error.