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In statistical hypothesis testing, the analogous concepts are known as type I and type II errors, where a positive result corresponds to rejecting the null hypothesis, and a negative result corresponds to not rejecting the null hypothesis. The terms are often used interchangeably, but there are differences in detail and interpretation due to ...
The critical region [C α, ∞) is realized as the tail of the standard normal distribution. Critical value s of a statistical test are the boundaries of the acceptance region of the test. [ 41 ] The acceptance region is the set of values of the test statistic for which the null hypothesis is not rejected.
Thyroid function tests (TFTs) is a collective term for blood tests used to check the function of the thyroid. [1] TFTs may be requested if a patient is thought to suffer from hyperthyroidism (overactive thyroid) or hypothyroidism (underactive thyroid), or to monitor the effectiveness of either thyroid-suppression or hormone replacement therapy.
The simplest case of a normal distribution is known as the standard normal distribution or unit normal distribution. This is a special case when μ = 0 {\textstyle \mu =0} and σ 2 = 1 {\textstyle \sigma ^{2}=1} , and it is described by this probability density function (or density): φ ( z ) = e − z 2 2 2 π . {\displaystyle \varphi (z ...
[22] [79] [87] As a result, LT 4 monotherapy may not result in a "truly biochemically euthyroid state." [23] Patients may express a preference for "low normal or below normal TSH values" [87] and/or T 4 and T 3 monitoring. The monitoring of other biomarkers that reflect the action of thyroid hormone on tissues has also been proposed. [15] [97] [22]
The p-value is the probability of obtaining results as extreme as or more extreme than those observed, assuming the null hypothesis (H 0) is true. It is also called the calculated probability. It is common to confuse the p-value with the significance level (α), but, the α is a
X follows a normal distribution with mean μ and variance σ 2 /n. s 2 (n − 1)/σ 2 follows a χ 2 distribution with n − 1 degrees of freedom. This assumption is met when the observations used for estimating s 2 come from a normal distribution (and i.i.d. for each group). Z and s are independent.
In the absolutely continuous case, probabilities are described by a probability density function, and the probability distribution is by definition the integral of the probability density function. [7] [4] [8] The normal distribution is a commonly encountered absolutely continuous