Estimation Prediction Test of Hypothesis

Inference may be defined as the process of drawing conclusions based on evidence and reasoning. It lies at the heart of the scientific method, for it covers the principles and

2000

Statistical inference is about using statistical data (x) to formulate an opinion about something that is dened well, but unknown ( y). Testing a hypothesis H about y is one of the possibilities, the estimation or prediction ofy is another one. We concentrate the attention on estimation or prediction in the sense that an opinion is required in the form

International Journal of Statistics in Medical Research, 2021

Statistical hypotheses testing is one of the basic direction of mathematical statistics the methods of which are widely used in theoretical research and practical applications. These methods are widely used in medical researches too. Scientists of different fields, among them of medical too, that are not experts in statistics, are often faced with the dilemma of which method to use for solving the problem they are interested. The article is devoted to helping the specialists in solving this problem and in finding the optimal resolution. For this purpose, here are very simple and clearly explained the essences of the existed approaches and are shown their positive and negative sides and are given the recommendations about their use depending on existed information and the aim that must be reached as a result of an investigation.

Le Centre pour la Communication Scientifique Directe - HAL - Ecole polytechnique, 2019

2014

Starting to teach inference early in an introductory statistics course means that the students have m ore time to assimilate the new concepts involved in estimation and hypothesis testing, especially if they are exposed to them in a sustained way throughout the semester. I teach a special section of the algebra-based course in which we start writing statistical hypotheses during the first week. We tell the students the general idea about testing hypotheses and that the details on how to calculate or approximate the p-value will depend on the context and the tools available. Randomization methods (permutation tests and bootstrapping) are introduced first because they require less background. After covering the basics of probability, the binomial distribution is used to do inference about one proportion. The classical methods using the normal, t-student and Chi-square distributions are studied at the end of the semester after these distributions have been introduced.

Paper, 2023

This paper presents scientific inference and its role in obtaining scientific knowledge. The presentation begins with the concept of scientific inference, followed by the specification of each types of scientific inference: deductive inference, inductive inference, and abductive inference. Next is discussed the reasoning process involved and the issues that arise in each type of scientific inference. The presentation in this paper ends with a discussion of how to infer causality in natural phenomena which is the main goal of science.

2016

What are we doing when we make inferences? I argue that to make an inference is to attach inferential force to an argument. Inferential force must be understood in analogy to assertoric force, and an argument is a structure of contents. I call this the “Force Account of inference.” I develop this account by first establishing two criteria of adequacy for accounts of inference. First, such accounts must explain why it is absurd to make an inference one believes to be bad. The upshot is that if someone makes an inference, she must take her inference to be good. Second, accounts of inference must explain why we cannot take our inferences to be good---in the sense that matters for inference---by merely accepting testimony to the effect that they are good. Next, I spell out the Force Account in detail, and I show that it meets these two criteria of adequacy. According to the Force Account, we make an inference by reflectively endorsing the inference as good. That allows us to understand ...

why is it difficult to understand statistical inference?", 2020

Difficulties in learning (and thus teaching) statistical inference are well reported in the literature. We argue the problem emanates not only from the way in which statistical inference is taught but also from what exactly is taught as statistical inference. What makes statistical inference difficult to understand is that it contains two logics that operate in opposite directions. There is a certain logic in the construction of the inference framework, and there is another in its application. The logic of construction commences from the population, reaches the sample through some steps and then comes back to the population by building and using the sampling distribution. The logic of application, on the other hand, starts from the sample and reaches the population by making use of the sampling distribution. The main problem in teaching statistical inference in our view is that students are taught the logic of application while the fundamental steps in the direction of construction are often overlooked. In this study, we examine and compare these two logics and argue that introductory statistical courses would benefit from using the direction of construction, which ensures that students internalize the way in which inference framework makes sense, rather than that of application.

Academia Biology, 2024

Many recent studies in evolutionary biology have expanded and refined definitions of biological evolution and natural selection. Current evolutionary models incorporate different adaptive and non-adaptive processes based on molecular genetic changes and how DNA is modified over time in unicellular species, or in germline versus somatic cells in metazoan species. Cogent arguments can be raised for the view that natural selection should be considered a biological law, consistent with quantitative mathematical equations that describe the fitness of individuals, as well as variations within and among populations. Evolution is an overarching framework that incorporates the laws of natural selection and clarifies why phenotypic variation can increase in prevalence and result in species adaptations. The conceptual framework for biological evolution incorporates many cohesive principles that collectively have predictive value. This framework will continue to evolve with improvements in high resolution technologies that enable us to examine both adaptive and non-adaptive changes that drive biological phenotypes.