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In August 1908, a white mob attacked Springfield, Illinois’s Black community, destroying homes and businesses and lynching two Black men. National outrage over the violence helped spur the movement that created the NAACP the following year.
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
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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.[1]: 94 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.
Negative testing is done to check that the product deals properly with the circumstance for which it is not programmed. The fundamental aim of this testing is to check how bad data is taken care of by the systems, and appropriate errors are shown to the client when bad data is entered. Both positive and negative testing play an important role. Positive testing ensures that the application does what it is implied for and performs each function as expected. Negative testing is opposite of positive testing. Negative testing discovers diverse approaches to make the application crash and handle the crash effortlessly.
Example
There are two basic techniques that help to write sufficient test cases to cover most of the functionalities of the system. Both these techniques are used in positive testing as well. The two parameters are:
Boundary indicates a limit to something. In this parameter, test scenarios are designed in such a way that it covers the boundary values and validates how the application behaves on these boundary values.
Example If there is an application that accepts Ids ranging from 0–255. Hence in this scenario, 0,255 will form the boundary values. The values within the range of 0–255 will constitute the positive testing. Any inputs going below 0 or above 255 will be considered invalid and will constitute negative testing.
The input data may be divided into many partitions. Values from each partition must be tested at least once. Partitions with valid values are used for positive testing. While partitions with invalid values are used for negative testing.
Example Numeric values from minus ten to ten are divided into two partitions: from minus ten to zero and from one to ten. If we need to test positive numeric values, then the first partition (from minus ten to zero) is used in negative testing.
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Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
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 →Mae Jemison, aboard Space Shuttle Endeavour in 1992.