Review of Alpha and Beta Risks in Hypothesis Testing

Would you reject the hypothesis H(0):MU = 69 versus the (one-sided) alternative H(1):MU > 69 on the basis of your observations, when testing at level ALPHA = .05?

This controversy over whether the alpha-stat or the pH-stat theory is does have practical anaesthetic relevance in patients who are rendered hypothermic (eg while on cardiopulmonary bypass). What is the pH level to aim for in these patients? It seems that the alpha-stat theory is now widely accepted. This is probably related to the intellectual attraction of the theoretical arguments because major differences in outcome between groups of patients managed by the pH-stat or the alpha-stat technique have not been clear. Cells are capable of functioning despite the presence of perturbation. Clinical studies have concentrated on which approach is best for the heart (myocardial outcome) and/or which approach is best for the brain (neurological outcome). a pCO₂ level. cerebral blood flow

Understanding Hypothesis Tests: Significance Levels (Alpha…

The smaller the alpha level, the smaller the area where you would reject the null hypothesis. So if you have a tiny area, there’s more of a chance that you will NOT reject the null, when in fact you should. This is a Type II error.
In other words, the more you try and avoid a Type I error, the more likely a Type II error could creep in. Scientists have found that an alpha level of 5% is a good balance between these two issues.

The imidazole has a degree of dissociation (referred to as alpha) of 0.55 in the intracellular compartment and this remains constant despite changes in temperature (ie the pK is changing with change in temperature). This theory about the constancy of the imidazole alpha value as proposed by Reeves and Rahn has been termed the imidazole alphastat hypothesis.

1.6 Alphastat Hypothesis - Anaesthesia MCQ - Anaesthesia …

The significance level (also known as the "critical value" or "alpha") you should use depends on the costs of different kinds of errors. With a significance level of 0.05, you have a 5% chance of rejecting the null hypothesis, even if it is true. If you try 100 different treatments on your chickens, and none of them really change the sex ratio, 5% of your experiments will give you data that are significantly different from a 1:1 sex ratio, just by chance. In other words, 5% of your experiments will give you a false positive. If you use a higher significance level than the conventional 0.05, such as 0.10, you will increase your chance of a false positive to 0.10 (therefore increasing your chance of an embarrassingly wrong conclusion), but you will also decrease your chance of a false negative (increasing your chance of detecting a subtle effect). If you use a lower significance level than the conventional 0.05, such as 0.01, you decrease your chance of an embarrassing false positive, but you also make it less likely that you'll detect a real deviation from the null hypothesis if there is one.

The alphastat hypothesis is about maintaining alpha which means that the net charge on all proteins is kept constant despite changes in temperature. This ensures that all proteins can function optimally despite temperature changes. The importance of pH is not just about intracellular trapping of metabolic intermediates (small molecule effect) but also about protein function (large molecule effect). This affects all proteins, though enzymes usually figure prominently as examples. So, to answer the question about why pH is so important in metabolism involves these two reasons.

1.6 - Alphastat Hypothesis - Anaesthesia MCQ

Seeing as the alpha level is the probability of making a Type I error, it seems to make sense that we make this area as tiny as possible. For example, if we set the alpha level at 10% then there is large chance that we might incorrectly reject the null hypothesis, while an alpha level of 1% would make the area tiny. So why not use a tiny area instead of the standard 5%?

Statistic, null hypothesis, & alpha?? | Yahoo Answers

An alpha level is the probability of a type I error, or you when it is true. A related term, , is the opposite; the probability of rejecting the alternate hypothesis when it is true.

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Alpha Level (Significance Level): What is it?

Would you reject the hypothesis H(0):MU = 72 versus the alternative H(1):MU =/= 72 on the basis of the observations, when testing at level ALPHA = .05?

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