Sentences with phrase «understand hypothesis testing»

Not exact matches

The principle difference that most people don't understand though is that «scientific theory» means that it's a hypothesis that's been repeatedly tested and supported with multiple pieces of evidence through many different trials and approaches.

«This study has greatly helped us understand how the Ebola virus replicates and should at least allow us to test some new drug leads and hypothesis,» he said.

«Testing these contrasting hypotheses was an opportunity to not only help people understand and plan for diversity changes after extreme weather events, but also to provide important data that would move the field of ecology forward.»

We want them to understand the scientific method, and to ask whether the author had a hypothesis and conducted an experiment that allowed them to test the hypothesis with a yes or no answer to a question.

The experiments we designed let us test and falsify a series of hypotheses, ultimately leading to a better understanding of heat flow between dissimilar materials.»

«Now we have a great theoretical framework to understand what generates them and to help us make and test more hypotheses.»

This has led to a demand for people who can understand data collection techniques and analyze vast amounts of data, categorize the data sets, develop models to test hypotheses that can then be used to develop drugs, and test potential candidates in animals.

The committee that conducted the study and wrote the report said that science knowledge is only one component of science literacy, which also encompasses understanding scientific practices, such as forming and testing hypotheses, and understanding science as a social process, such as the role of peer review.

The program get teams out of their comfort zone of the lab to conduct discovery interviews across key stakeholders to understand needs and test hypotheses, develop and validate value propositions and business models, define «killer» questions and experiments, and create pitches to target funders.

By taking a picture of the accretion disk around a black hole, the EHT will test this hypothesis and work toward a better understanding of the processes that allow accretion disks to form and black holes to grow.

Another possible explanation is that non-Hispanic blacks may be less susceptible to the metabolic effects of sugars, although this hypothesis needs to be further tested.12, 54,56 Finally, although observational studies are important in improving our understanding of nutrient - disease relationships, they should not be directly interpreted as evidence of causal relationships without considering other lines of evidence.57

The typology includes logical problems, algorithmic problems, story problems (which have underlying algorithms with a story wrapper that amounts to an algorithmic problem), rule - using problems, decision - making problems (e.g., cost - benefit analysis), troubleshooting (systematically diagnosing a fault and eliminating a problem space), diagnosis - solution problems (characteristic of medical school and involving small groups understanding the problem, researching different possible causes, generating hypotheses, performing diagnostic tests, and monitoring a treatment to restore a goal state), strategic performance, case analysis (characteristic of law or business school and involving adapting tactics to support an overall strategy and reflecting on authentic situations), design problems, and dilemmas (such as global warming, which are complex and involve competing values and which may have no obvious solutions).

Interviews with ecosystem scientists support the team's ability to develop EcoXPT so that students can authentically test their own hypotheses so as to better understand causal patterns they could previously only observe, thereby extending their comprehension of underlying causal relationships.

The typology includes: logical problems, algorithmic problems, story problems (which are algorithmic problems with a story wrapper), «rule - using» problems, decision - making problems (e.g., cost - benefit analysis), troubleshooting (systematically diagnosing a fault, eliminating a problem space), «diagnosis - solution» problems (characteristic of medical school, which involve small groups understanding the problem, researching different possible causes, generating hypotheses, performing diagnostic tests, and monitoring a treatment to restore a goal state), strategic - performance, case analysis (characteristic of law or business school, which involve adapting tactics to support an overall strategy and reflecting on authentic situations), design problems, and dilemmas (such as global warming, which are complex and involve competing values, and which may have no solutions).

I want to know whether children can understand stories, if they can explain their own reasoning when they do a math problem, if they can formulate their observations and test hypotheses in their science classes.

In this post, we want to look at the least used and understood design question, Design Question 4, Helping Students Generate and Test Hypotheses and how DQ4 works with Design Question -LSB-...]

In this post, we want to look at the least used and understood design question, Design Question 4, Helping Students Generate and Test Hypotheses and how DQ4 works with Design Question 2, Helping Students Interact with New Knowledge and Design Question 3, Helping Students Practice and Deepen New Knowledge to provide us with a pathway to the Common Core State Standards.

An understanding of the qualitative research principles, the dynamics associated with diversity and change and the need to study problems that are relevant in real settings while systematically inquiring, making hypotheses and testing these hypotheses; use as a vehicle for empowering teachers and learners.

I will explain how some activities are designed to introduce students to new content in Chapter 2 how some activities are designed to help students practice and deepen their understanding of new content in Chapter 3, and how some activities are designed to help students generate and test hypotheses about content in Chapter 4.

«This book reminds us that we need to go back to models of learning and use them to help students see similarities and differences, learn how to summarize and take notes, practice deliberately, use imagery to build a deeper conceptual understanding on which they can «hang» surface level knowledge, learn from one another, solve problems, generate and test hypotheses, and give and receive feedback,» wrote education professor and author John Hattie in his foreword to the publication.

However that is a statistical non-sequitur, based on a poor understanding of frequentist hypothesis testing, unless the test can be shown to have adequate statistical power (which seems never to be mentioned by those claiming a pause in warming).

I think understanding the mechanisms enables formation & testing of sensible hypotheses & also comprehension of what worked & why in manner that makes extension to new settings possible.

Because the readership is well - informed, they are more likely to welcome a focus on the particular hypotheses in question than on theories of hypothesis testing, or the proper way to conduct science, both of which they probably already understand quite well.

The hypothesis put forward is the author's novel method of «joint estimation» and the test is «does it increase our understanding?»

I think it is fair to say that we do not know all the mechanisms involved between the sun and our climate (for example, things like the GCR cloud hypothesis being tested at CERN), and IPCC has conceded that its «level of scientific understanding of solar (natural) forcing is low».

I fully understand the two points you raised even before I proposed this method of hypothesis testing.

The NIPCC doesn't understand some stuff about hypothesis testing.

Scientific understanding that allows for hypothesis testing however is deserving of more respect than understandings that merely seeks to describe and perhaps project (without testing) what's been observed so far.

«That's important for understanding causes of megadroughts, and it's important for climate modelers to test hypotheses of climate forcing and change.»

Another reason that hypothesis II and III are not as plausible in my view is that for them to be correct, our understanding of radiative physics etc., which have been tested experimentally and by observations (e.g. spectra of outbound IR radiation) must be fundamentally wrong.

As well as testing an hypothesis by comparing observations to the observable consequences of the hypothesis, it's also possible to «criticise» the hypothesis by questioning its internal logic, its consistency with other better understood theories or its underlying assumptions.

«Models» are tools to help scientists understand patterns, and formulate hypotheses for further testing.

The hypothesis that the post 1998 period is consistent with the existing understanding of anthropogenic climate change is evaluated with a test statistic that evaluates the null hypothesis that the long - run relationship between global surface temperature and radiative forcing is unchanged after 1998.

That is only true for physicists that don't understand statistical hypothesis testing.

It is my understanding that science is the process of developing a hypothesis, testing that hypothesis and then putting everything out in the open for others to prove or disprove.

In conclusion, the thesis advocates that GCMs be used and developed uncompromisingly for «Hypothesis testing, numerical experiments, to understand how the climate system works, including its sensitivity to altered forcing,» such a policy to continue until climate model building becomes better understood.

Quite egalitarian, so in fact contrarians, scientists who hold ideas outside of the mainstream can prosper provided their ideas have some factual basis and use the scientific method (Scientific method: based on existing obervations pose an hypothesis; using new observations or experiments, test the predictions of that hypothesis; on the basis of the new data either reject the hypothesis or modify it to fit the better understanding, or accept that the initial hypothesis was right at which point it becomes a «theory» or explanatory model).

The theoretical foundations of model selection are often poorly understood by practitioners of null hypothesis testing, and even many proponents of Chamberlin's method may not fully appreciate its historical basis.

My understanding is that the notion of null versus alternate hypotheses came from the statistical testing field where in general a null hypothesis is an assertion that some phenomenon will be constrained to a subset, often a subspace, of the total universe of possible observations.

«It's paramount to understand that a pivot isn't simply a change in one element of the business model... but rather a change precipitated by something the founder has learned and validated to be true or untrue about a hypothesis she has tested.»

We sought to understand these presentations in terms of the doctor — patient relationship, specifically to test the hypothesis that such patients have insecure emotional attachment.

Future studies could test hypotheses based on this model to improve our understanding of the development of adolescents» depressive symptoms in both boys and girls.

To improve our understanding of the development of depressive symptoms, future research could test hypotheses in which factors from different levels interact, i.e., cognitions, genetics, environment, affect, negative life experiences, as suggested by the cognitive vulnerability - transactional stress model (Hankin and Abramson 2001).

Although this could not be tested in the current study, given the theoretical importance of attachment security to child emotional functioning (e.g., Cassidy, 1994), as well as the well - established link between emotional dysregulation and childhood anxiety, another hypothesis is that attachment security relates to anxiety via children's emotional capacities, including children's emotion understanding and regulation.