Failure testing

Examples of Failure testing in the following topics:

• Hypothesis testing for a proportion

  • To apply the normal distribution framework in the context of a hypothesis test for a proportion, the independence and success-failure conditions must be satisfied.
  • In a hypothesis test, the success-failure condition is checked using the null proportion: we verify np 0 and n(1 − p 0 ) are at least 10, where p 0 is the null value.
  • Set up a one-sided hypothesis test to evaluate this claim.
  • In a one-proportion hypothesis test, the success-failure condition is checked using the null proportion, p 0 = 0.5: np 0 = n(1 − p 0 ) = 500 × 0.5 = 250 > 10.
  • The p-value for the test is shaded.

• Renal Disease and Failure

  • Renal failure uremia is a syndrome of renal failure characterized by elevated levels of urea and creatinine in the blood.
  • Renal failure (also kidney failure or renal insufficiency) is a medical condition in which the kidneys fail to adequately filter waste products from the blood.
  • Renal failure uremia is a syndrome of renal failure that includes elevated blood urea and creatinine levels.
  • Acute renal failure can be reversed if diagnosed early.
  • Diagnostic tests include BUN and plasma creatinine level tests.

• The Automation Ratio

  • Regression testing means testing for the reappearance of already-fixed bugs.
  • Regression testing is not a panacea.
  • Projects with regression test suites often have a corollary rule: don't commit any change that causes tests to fail.Such failures are easiest to spot if there are automatic nightly runs of the entire test suite, with the results mailed out to the development list or to a dedicated test-results mailing list; that's another example of a worthwhile automation.
  • This situation was due to a failure on all our parts to consider the automation ratio.
  • The point is not that having strict requirements to write tests is bad, nor that writing your test system as a Bourne shell script is necessarily bad.

• Papanicolaou Test

  • The Pap smear is a test used to determine the health of the cervical canal and is an important test in cancer prevention.
  • The BabeșPapanicolaou test (also called Pap smear, Pap test, cervical smear, or smear test) is a screening test used to detect potentially pre-cancerous and cancerous processes in the endocervical canal (transformation zone) of the female reproductive system.
  • The test was invented by and named after the prominent Greek doctor Georgios Papanikolaou.
  • The test may also detect infections and abnormalities in the endocervix and endometrium.
  • Failure of prevention of cancer by the Pap test can occur for many reasons, including not getting regular screening, lack of appropriate follow up of abnormal results, and sampling and interpretation errors.

• Heart Failure

  • Heart failure is defined as the inability of the heart to supply blood to the organs of the body.
  • Heart failure (HF), often called congestive heart failure (CHF), is generally defined as the inability of the heart to supply sufficient blood flow to meet the needs of the body.
  • The condition is diagnosed with echocardiography and blood tests.
  • Heart failure may also occur when the body's requirements for oxygen and nutrients are increased and the demand outstrips what the heart can provide, (termed high-output cardiac failure).
  • Echocardiography is commonly used to support a clinical diagnosis of heart failure.

• Standardized Tests

  • Standardized tests are identical exams always administered in the same way so as to be able to compare outcomes across all test-takers.
  • Standardized tests are usually created by a team of test experts from a commercial testing company in consultation with classroom teachers and university faculty.
  • Standardized tests are perceived as being "fairer" than non-standardized tests and more conducive to comparison of outcomes across all test takers.
  • Some recent standardized tests incorporate both criterion-referenced and norm-referenced elements in to the same test.
  • David Wechsler, the creator of the Wechsler intelligence scales, thought intelligence measurements needed to address more than just one factor and also that they needed to take into account "non-intellective factors" such as fear of failure or lack of confidence.

• Assumption

  • When you perform a hypothesis test of a single population mean µ using a Student's-t distribution (often called a t-test), there are fundamental assumptions that need to be met in order for the test to work properly.
  • When you perform a hypothesis test of a single population mean µ using a normal distribution (often called a z-test), you take a simple random sample from the population.
  • The population you are testing is normally distributed or your sample size is sufficiently large.
  • When you perform a hypothesis test of a single population proportion p, you take a simple random sample from the population.
  • You must meet the conditions for a binomial distribution which are there are a certain number n of independent trials, the outcomes of any trial are success or failure, and each trial has the same probability of a success p.

• Testing a Single Proportion

  • Here we will evaluate an example of hypothesis testing for a single proportion.
  • Test at a 5% significance level.
  • Therefore, we can perform a one proportion $z$-test to test this belief.
  • To meet this condition, both the success and failure products must be larger than 10 ($p_0$ is the value of the null hypothesis in decimal form. )
  • The conditions are satisfied, so we will use a hypothesis test for a single proportion to test the null hypothesis.

• Comparing Two Independent Population Proportions

  • The number of successes is at least five and the number of failures is at least five for each of the samples.
  • A hypothesis test can help determine if a difference in the estimated proportions (PA − PB) reflects a difference in the population proportions.
  • To conduct the test, we use a pooled proportion, pc.
  • Two types of medication for hives are being tested to determine if there is a difference in the proportions of adult patient reactions.
  • Test at a 1% level of significance.

• Quality costs

  • The four major types of quality costs are prevention, appraisal, internal failure, and external failure.
  • Appraisal costs include the inspection and testing of raw materials, work-in-process, and finished goods.
  • Inspection and testing of raw materials is very important, since substandard raw materials lead to substandard products.
  • Finished goods and work-in-process inventory also need inspecting and testing.
  • For example, a wooden baseball bat manufacturer may test 10 out of every 100 bats to check that they meet strength standards.