Civil Rights
Movements, leaders, victories and the continuing fight for equality.
q2K BHP
Black History Portal
THE BHP WIRE —
HIDDEN TRUTHS
What's New!
Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
Black towns, communities, institutions and places where history happened.
Moments that changed communities, movements, institutions and the nation.
Wilmington, North Carolina once had a thriving Black middle class and an elected interracial government. In 1898 white supremacists used violence to overthrow that government, kill Black residents and drive many others from the city.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
Negative testing is a method of testing an application or system to improve the likelihood that an application works as intended/specified and can handle unexpected input and user behavior. Invalid data is inserted to compare the output against the given input. Negative testing is also known as failure testing or error path testing. When performing negative testing exceptions are expected. This shows that the application is able to handle improper user behavior. Users input values that do not work in the system to test its ability to handle incorrect values or system failure.
A negative test can relate to: Negative diagnostic test, a medical test in which the target parameter that was evaluated was not present Negative test variation, a software stress test designed to determine the response of the system outside of normal parameters Negative testing
In medicine, statistics, and epidemiology, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true positives and specificity is a measure of how well a test can identify true negatives: Sensitivity (true-positive ratio) is the probability of a positive test result, conditioned on the individual truly being positive. Specificity (true-negative ratio) is the probability of a negative test result, conditioned on the individual truly being negative. If the true status of the condition has been difficult to determine without testing, but the existing test is invasive, costly, or otherwise difficult to access, a candidate test’s sensitivity and specificity can be defined relative to a "gold standard test" that is assumed to always produce the truly correct outcome. In all testing, in both medical diagnosis and screening, there is usually a trade-off between sensitivity and specificity: higher sensitivity usually means lower specificity, and vice versa. A test that reliably detects the presence of a condition, resulting in a high number of true positives and a low number of false negatives, will have high sensitivity. This is especially important when the consequences of failing to treat the condition are serious and/or the treatment is highly effective with minimal side effects. A test that reliably excludes individuals who do not have the condition, resulting in many true negatives and few false positives, will have high specificity. This is especially important when people who are identified as having a condition may be subjected to more testing, expense, stigma, anxiety, etc. American biostatistician Jacob Yerushalmy introduced the terms "sensitivity" and "specificity" in 1947. There are different definitions within laboratory quality control, wherein "analytical sensitivity" is defined as the smallest amount of substance in a sample that can accurately be measured by an assay (synonymously to detection limit), and "analytical specificity" is defined as the ability of an assay to measure one particular organism or substance, rather than others. However, this article deals with diagnostic sensitivity and specificity as defined above.
A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative). They are also known in medicine as a false positive (or false negative) diagnosis, and in statistical classification as a false positive (or false negative) error. 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 differences between medical testing and statistical hypothesis testing.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Brown v. Board of Education in 1954.
