Type I and type II errors

AGUILAR, Janica Mara Y. BERBOSO, Kevin Leo C. CAYUBE, Katherine Kate Z. Prof. Ria Sagum Advisor PHILEX: Philippine Land Law Expert Chatbot Abstract Keywords: Chatbot, Natural Language Processing (NLP), First-Order Predicate Logic, Natural Language Generation (NLG), Precision and Recall The researchers tend to work on an expert system with integration of Philippine land laws. They chose to focus on land laws since one of the major problems of the Filipinos who seek law

c. It should appear in the tail signifying a positive relationship, with .05 in that tail. d. It should appear in the tail signifying a negative relationship, with .05 in that tail. An administrator working in a child guidance center tests the hypothesis that family income will be related to number of treatment sessions attended. Her sample size is large, and her level of significance is .05. How should her critical region appear in her theoretical sampling distribution? a. It should be split

Before conducting a statistical analysis, every researcher needs to measure the depended variable. The measurement depends on the type of variable. Different types are measured differently. So, the researcher has to decide on the appropriate scale of measurement. There are typically four levels of measurement: nominal, ordinal, interval, and ratio. 1. The purpose of the nominal scale is for identification and represents the lowest level of measurement. With a nominal scale, the researcher only

The problem to be addressed is the recognition of incarceration impacting adversely on some prisoners, while others cope relatively well with the stresses of confinement to a maximum security prison subculture. This topic will be analyzed using a quantitative approach. Since the publication of The Prison Community (Clemmer, 1940) virtually thousands of books and articles have been published on prison life and the adjustment of prisoners to their confinement. One article highlights this exact problem

interpreted correctly. In particular, Type I or Type II errors may be increased or decreased incorrectly. Nimon’s (2012) article focused on assumptions associated with substantive statistical analyses across the

hypothesis testing, Type I error isA) always equal to 5 percent. B) always smaller or equal to 5 percent. C) the probability of rejecting H0 when H0 is true. D) the probability of rejecting H0 when H1 is true. Answer: C2. In hypothesis testing, Type II error isA) equal to 1 - probability of committing Type I error. B) equal to 5 percent or more. C) the probability to accept H0 when H0 is true. D) the probability to accept H0 when H1 is true. Answer: D3. The probability of Type I error, , and the probability

also used to calculate the margin of error (Salkind, 2014). Lastly, the critical value is determined from the alpha or significance value of the hypothesis test (Salkind, 2014). 6. Given the following information, would your decision be to reject or fail to reject the null hypothesis? Setting the level of significance at .05 for decision making, provide an explanation for your conclusion. a. The null hypothesis that there is no relationship between the type of music a person listens to and his

Today’s session is a little bit different, we will be talking about statistical significance. Statistical significance is the level of risk one is willing to take to reject or accept a null hypothesis while it is true and it separate random error from systematic error. When doing a study or research, the statistical significance shows that the difference obtained were not caused by chance. Inferential statistics, the T-test, partition noise from bias by studying a random sample than the population in

studying medication adherence. GMMs (and their GBTM counterparts) provide a sophisticated statistical method by which to capture the heterogeneity within a given population, and often map onto how many researchers conceptualize growth, in that, different types of individuals likely exhibit different trajectories of behavior. Given the rapid adoption and application of such models within the clinical literature, and the fact that this modeling technique is relatively novel, it stands to reason that there

of our day. Business and medical professionals, sociologists and scientists alike all utilize statistics in order to determine probabilities of outcomes. The purpose of this paper is to discuss what I have learned about statistics in during this class covered during the last five weeks. The topics I will cover inform the class are descriptive statistics, inferential statistics, hypothesis development and testing, selection of appropriate statistical tests and evaluating statistical results. The