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Related Concept Videos

Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

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The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
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Hypothesis: Accept or Fail to Reject?01:17

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The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null...
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What is a Hypothesis?01:14

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A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about a  property of the population. It can be stated for any field observations or experiments. A hypothesis statement cannot be said to be right or wrong as it is merely a statement. It needs to be tested through an elaborate data collection process and an appropriate statistical test. A hypothesis should be a general but not a vague...
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Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
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Statistical Hypothesis Testing01:16

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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
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HDL hypothesis: where do we stand now?

Sayed M Tariq1, Mandeep S Sidhu, Peter P Toth

  • 1Department of Medicine, Albany Medical Center, Albany Medical College, Albany, NY, USA, TariqS@mail.amc.edu.

Current Atherosclerosis Reports
|March 13, 2014
PubMed
Summary
This summary is machine-generated.

Despite evidence linking high-density lipoprotein cholesterol (HDL-C) to lower coronary artery disease (CAD) risk, therapies to raise HDL-C have not consistently reduced cardiovascular events. Current trials show insufficient evidence to recommend HDL-targeted therapy for CAD event reduction.

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Area of Science:

  • Cardiology
  • Biochemistry
  • Epidemiology

Background:

  • Epidemiological studies, including the Framingham Heart Study, show an inverse relationship between high-density lipoprotein cholesterol (HDL-C) and coronary artery disease (CAD) risk.
  • Low HDL-C levels are an independent predictor of subsequent CAD events.
  • Despite this, therapeutic attempts to raise HDL-C have not consistently reduced major adverse cardiovascular events like death, myocardial infarction (MI), or stroke.

Purpose of the Study:

  • To evaluate the scientific viability of the "HDL hypothesis" which posits that raising HDL-C can lower cardiovascular events.
  • To review recent major randomized trials investigating HDL-C-raising therapies and their impact on cardiovascular outcomes.
  • To discuss the clinical implications of these findings for modern pharmacotherapy in managing cardiovascular risk.

Main Methods:

  • Review of data from major randomized controlled trials including AIM-HIGH, HPS2-THRIVE, ILLUMINATE, and dal-OUTCOMES.
  • Analysis of trial outcomes concerning cardiovascular event reduction in patients receiving HDL-C-targeted therapies.
  • Consideration of specific patient populations and baseline treatment parameters within these trials.

Main Results:

  • Recent trials (AIM-HIGH, HPS2-THRIVE) did not demonstrate a reduction in cardiovascular events in patients with stable ischemic heart disease treated with extended-release niacin and simvastatin.
  • These trials excluded specific populations, such as those with acute coronary syndromes or significant residual dyslipidemia on suboptimal statin therapy, limiting extrapolation of findings.
  • Current clinical trial evidence is insufficient to support HDL-targeted therapy for additional event reduction in CAD patients.

Conclusions:

  • The efficacy of raising HDL-C to reduce cardiovascular events remains uncertain.
  • Recent trial data raise concerns about the "HDL hypothesis" and its clinical application.
  • Further research and careful consideration of patient populations are needed to clarify the role of HDL-C-targeted therapies in cardiovascular disease management.