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Cousin
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For other uses, see Cousin (disambiguation).
"Cousins" redirects here. For other uses, see Cousins (disambiguation).
"First cousin once removed" redirects here. For the documentary film, see First Cousin Once Removed.
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Commonly, "cousin" refers to a "first cousin" or equivalently "full cousin", people whose most recent common ancestor is a grandparent.[1] A first cousin used to be known as a cousin-german, though this term is rarely used today.[2]
More generally, cousin is a type of familial relationship in which people with a known common ancestor are both two or more generations away from their most recent common ancestor. This distinguishes a cousin from an ancestor, descendant, sibling, aunt, uncle, niece, or nephew.[3]
Systems of "degrees" and "removals" are used in the English-speaking world to describe the exact relationship between two cousins (in the broad sense) and the ancestor they have in common.[4] Various governmental entities have established systems for legal use that can precisely specify kinship with common ancestors any number of generations in the past.[citation needed] Common usage often eliminates the degrees and removals, and refers to people with common ancestry as simply "distant cousins" or "relatives".[5]
Contents
[hide]
1 Basic definitions
1.1 Multiplicities
1.2 Gendered relationships
1.3 Non blood relations
1.4 Examples
2 Relationship charts
3 Cousin marriage
4 Genetics
5 Additional terms
6 See also
7 References
8 External links
Basic definitions[edit]
Family tree. Cousins are colored green. Generations are shown by alternating stripes of gray and white.
People are related with a type of cousin relationship if they share a common ancestor and the most recent common ancestor is two or more generations away from both people. This means neither person is an ancestor of the other, they do not share a parent (siblings), and neither is a sibling of a common ancestor (aunts/uncles and nieces/nephews).[3]
The cousin relationship is further detailed by degree and removal. For example the second cousin once removed relationship is a second-degree cousin with one removal.
The removal of the cousin relationship is the number of generations the cousins are apart. When the cousins are separated by a different number of generations from the most recent common ancestor, the cousin relationship is removed. The difference between the number of generations for each cousin is the removal.[4] For example if the most recent common ancestor is 2 generations prior for one person and 3 generations prior for the other (one person's grandfather is the other person's great-grandfather) or the most recent common ancestor is 3 generations prior for one person and 4 generations prior for the other (one person's great-grandfather is the other person's great-great-grandfather) the cousins are separated by one generation and therefore once removed. Note that two people can be removed but be around the same age due to differences in birth dates of parents children and other relevant ancestors.[4][6]
The degree of the cousin relationship is the number of generations prior to the parents before a most recent common ancestor is found. If the cousins are removed, the smaller number of generations to the most recent common ancestor is used to determine the degree of the cousin relationship.[4] For example if one of the cousins has to go back one generation beyond their parents (the grandparents) before finding the most recent common ancestor and the other has to go back one or more they are first cousins. If one had to go back two generations beyond the parents (great grandparents) and the other had to go back two or more they would be second cousins[6][4].
Multiplicities[edit]
Double cousins arise when two siblings of one family mate with two siblings of another family. This may also be referred to as 'Cousins on both sides.' The resulting children are related to each other through both of their parents and are thus doubly related. Double first cousins share both sets of grandparents and have twice the degree of consanguinity of ordinary first cousins.
Half cousins are descended from half sibling. The children of two half siblings are first half cousins. If half siblings have children with another pair of half-siblings the resulting children would be double half first cousins.
Gendered relationships[edit]
A maternal cousin is a cousin that is related to the mother's side of the family while a paternal cousin is a cousin that is related to the father's side of the family. Unlike all the other cousin relationships discussed thus far this relationship is not necessarily reciprocal as the maternal cousin of one person could be at the paternal cousin of the other.
Parallel and cross cousins on the other hand are reciprocal relationships. Parallel cousins are descended from same-sex sibling. Cousins that are related to same sex siblings of their most recent common ancestor are parallel cousins. A parallel first cousin is either the paternal cousin on the father's side of the family or the maternal cousin on the mother's side of the family. Cross cousin's are descendants from opposite-sex sibling. A cross first cousin is either the maternal cousin on the father's side of the family or the paternal cousin on the mother side of the family.
Non blood relations[edit]
Stepcousins are either stepchildren of an individual's aunt or uncle or nieces and nephews of one's stepparent. Cousins in law are the cousins of a person's spouse or the spouse of a person's cousin. Neither of these relationships have consanguinity.
Examples[edit]
Example family tree
Jason Beatrice
James Helen Eugene Mary
Nancy Joseph Julie Roger
Gordon Laura Christina Matt
Sam Lyla
First cousins
A person shares a first cousin or cousin relationship with the children of their parents' siblings. Cousins share at least one set of grandparents.[6] In the example family tree, Joseph and Julie are first cousins.
Second cousins
A person shares a second cousin relationship with the children of their parents' cousins. Second cousins share at least one set of great-grandparents.[6] In the example family tree, Gordon and Matt are second cousins.
Third cousins
A person shares a third cousin relationship with the children of their parents' second cousins. Third cousins share at least one set of great-great-grandparents.[6] In the example family tree, Sam and Lyla are third cousins.
First cousins once removed
A person shares a first cousins once removed relationship with their parents' cousins and their cousins' children. At least one set of one person's grandparents are the great-grandparents of the other person.[6] In the example family tree, Gordon and Julie, as well as Joseph and Matt, are first cousins once removed.
First cousins twice removed
A person shares a first cousins twice removed relationship with their grandparents' cousins and their cousins' grandchildren. At least one set of one person's grandparents are the great-great-grandparents of the other person.[6] In the example family tree, Sam and Julie, as well as Joseph and Lyla, are first cousins twice removed.
Second cousins once removed
A person shares a second cousin once removed relationship with their parents' second cousins and their second cousins' children. At least one set of one person's great-grandparents are the great-great-grandparents of the other person.[6] In the example family tree, Sam and Matt, as well as Gordon and Lyla, are second cousins once removed.
Maternal first cousins
A person's maternal first cousins are the children of their mother's siblings. In the example family tree, Julie is Joseph's maternal first cousin.
Paternal first cousins
A person's paternal first cousins are the children of their father's siblings. In the example family tree, Joseph is Julie's paternal first cousin.
Cross first cousins
A person shares a cross cousin relationship with their first cousin when the children of the most recent common ancestor are of different sexes. For cross first cousins the parents of each cousin are siblings and of different sexes. [6] In the example family tree, Joseph and Julie are cross first cousins.
Relationship charts[edit]
A "table of consanguinity", is helpful in identifying the degree of cousin relationship between two people using their most recent common ancestor as the reference point. Cousinship between two people can be specifically described in degrees and removals by determining how close, generationally, the common ancestor is to each person.[7][4][6]
If the relative's → Parent Grandparent Great-grandparent Great-great-grandparent Great-great-great-grandparent Great-great-great-great-grandparent
Is the subject's
↓ Then the relative is the subject's ↘
Parent Sibling Niece/Nephew Grandnephew/Grandniece Great-grandnephew/Great-grandniece Great-great-grandnephew/Great-great-grandniece Great-great-great-grandnephew/Great-great-great-grandniece
Grandparent Uncle/Aunt 1st cousins 1st cousins once removed 1st cousins twice removed 1st cousins three times removed 1st cousins four times removed
Great-grandparent Granduncle/Grandaunt 1st cousins once removed 2nd cousins 2nd cousins once removed 2nd cousins twice removed 2nd cousins three times removed
Great-great-grandparent Great-granduncle/Great-grandaunt 1st cousins twice removed 2nd cousins once removed 3rd cousins 3rd cousins once removed 3rd cousins twice removed
Great-great-great-grandparent Great-great-granduncle/Great-great-grandaunt 1st cousins three times removed 2nd cousins twice removed 3rd cousins once removed 4th cousins 4th cousins once removed
Great-great-great-great-grandparent Great-great-great-granduncle/Great-great-great-grandaunt 1st cousins four times removed 2nd cousins three times removed 3rd cousins twice removed 4th cousins once removed 5th cousins
Canon Law Relationship Chart. See an example of how to use chart.
Another visual chart used in determining the legal relationship between two people who share a common ancestor is a "canon law relationship chart".
The chart is the same as the previous chart with the exception that the chart is symmetrical and has been rotated 45° so that the common progenitor (most recent common ancestor) is placed at the peak. Since the graph is symmetrical the placement of either party in the realationship on either side of the graph is arbitrary. The graph does not distinguish between parents and children, or aunts/uncles and nieces/nephews. For these non symmetrical relationships the relationship of the person from the most recent generation is displayed (i.e. children and nieces/nephews).
Cousin marriage[edit]
Main article: Cousin marriage
Cousin marriage is important in several anthropological theories which often differentiate between matriarchal and patriarchal parallel and cross cousins.
Currently about 10% and historically as high as 80% of all marriages are between first or second cousins.[8][9] Cousin marriages are often arranged.[8][9][10][11][12] Anthropologists believe it is used as a tool to strengthen the family, conserve its wealth, protect its cultural heritage, and retain the power structure of the family and its place in the community. Some groups encourage cousin marriage while others attach a strong social stigma to it. In some regions in the Middle East over half of all marriages are between first and second cousins. Some of the countries in this region this may exceed 70%.[13] Just outside this region it is often legal but infrequent. In other places it is legally prohibited and culturaly equivalent to incest.[14][15] Supporters of cousin marriage often view the prohibition as discrimination,[16][17] while opponents cite the potential immorality.[18]
Genetics[edit]
Married couples that possess higher than normal consanguinity, shared identical DNA and genetic material, have an increased chance of sharing genes for recessive traits. The percentage of consanguinity between any two individuals decreases fourfold as the most recent common ancestor recedes one generation. First cousins have four times the consanguinity of second cousins, while first cousins once removed have half that of first cousins.
Double first cousins share both sets of grandparents and have twice the degree of consanguinity of ordinary first cousins; genetically, they are as related as half-siblings. Children of these marriages may have an increased risk of genetic disorders, particularly if their parents both carry a harmful recessive mutation.[citation needed] In a scenario where two monozygotic (identical) twins mate with another pair of monozygotic twins, the resulting double cousins would test as genetically similar as brothers or sisters.
Additional terms[edit]
The following is a list of less common cousin terms.
Term
Definition
Example
Chart
Double cousin Double cousins arise when two siblings of one family mate with two siblings of another family. Joseph and Julie are double first cousins because each is related through their mother's family and also their father's family, the result of a brother and sister (Helen and Eugene) having married another brother and sister (James and Mary). For Joseph and Julie, each has a mother who is an aunt by blood of the other and a father who is an uncle by blood of the other.
Gary Glenda
Jason Beatrice
James Helen Eugene Mary
Joseph Julie
Half-cousin Half-cousins are the children of two half-siblings, and their respective spouses. Joseph and Lilian are half cousins because their parents (Helen and Charles) are half-siblings, their grandmother (Beatrice) having remarried.
Jason Beatrice Anthony
James Helen Charles Janet
Joseph Lilian
Stepcousin Stepcousins are either stepchildren of an individual's aunt or uncle or nieces and nephews of one's stepparent. Joseph and Rachel are stepcousins because Joseph's uncle (Eugene) has become Rachel's stepfather as a result of Rachel's mother (Corinda) having remarried Eugene.
Jason Beatrice
James Helen Eugene Corinda Colin
Joseph Rachel
Cousin-in-law A cousin-in-law is the spouse of an individual's cousin or the cousin of one's spouse. Joseph and Roger are first cousins-in-law to each other because Roger's wife (Julie) is Joseph's first cousin.
Jason Beatrice
James Helen Eugene Mary
Joseph Julie Roger
See also[edit]
Collateral descendant
Consanguinity
Cousin marriage
Family
Parallel and cross cousins
Sibling
Second-degree relative
References[edit]
Jump up ^ "Cousin". Brewer's Dictionary of Phrase & Fable. Chambers Harrap Publishers. 2013. 19.
Jump up ^ "Cousin-german definition and meaning | Collins English Dictionary". www.collinsdictionary.com. Retrieved 2018-02-26.
^ Jump up to: a b "Definition of Cousin by Merriam-Webster". merriam-webster.com. Merriam-Webster.
^ Jump up to: a b c d e f A Dictionary of Genetics. Oxford University Press. 2013. 8.
Jump up ^ "Definition of cousin in English by Oxford Dictionaries". oxforddictionaries.com. Oxford University Press.
^ Jump up to: a b c d e f g h i j "Genetic And Quantitative Aspects Of Genealogy – Types Of Collateral Relationships". Genetic-genealogy.co.uk. Retrieved 28 October 2014.
Jump up ^ "What is a First Cousin, Twice Removed?". Genealogy.com. Retrieved Sep 26, 2015.
^ Jump up to: a b Kershaw, Sarah (26 November 2009). "Shaking Off the Shame". The New York Times.
^ Jump up to: a b Richard Conniff. "Go Ahead, Kiss Your Cousin."
Jump up ^ Bittles, Alan H. (May 2001). A Background Summary of Consanguineous Marriage (PDF) (Technical report). Edith Cowan University.
Jump up ^ Bittles 1994, p. 567
Jump up ^ Bittles and Black 2009, Section 7
Jump up ^ Dr. Alan Bittles; Dr. Michael Black. "Global prevalence". consang.net.
Jump up ^ The Surprising Truth About Cousins And Marriage
Jump up ^ Diane B. Paul and Hamish G. Spencer. "It's Ok, We're Not Cousins by Blood."
Jump up ^ "Final Thoughts". Cousin Couples. Retrieved 4 June 2016.
Jump up ^ Brandon Keim (23 December 2008). "Cousin Marriage OK by Science". Wired.
Jump up ^ The Love That Dare Not Speak Its Surname: What's wrong with marrying your cousin?
External links[edit]
Look up cousin in Wiktionary, the free dictionary.
European kinship system
Genealogy.com definition of various cousins
Genealogy.com: What makes a cousin?
Genetic Genealogy
Family Relationship Chart
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The Regulating Act of 1773 created the office with the title of Governor-General of the Presidency of Fort William, or Governor-General of Bengal to be appointed by the Court of Directors of the East India Company (EIC). The Court of Directors assigned a Council of Four (based in India) to assist The Governor General, and decision of council was binding on Governor General during 1773-1784.
The Saint Helena Act 1833 (or Government of India Act 1833) re-designated the office with the title of Governor-General of India.
After the Indian Rebellion of 1857, the company rule was brought to an end, and the British India along with princely states came under the direct rule of the Crown. The Government of India Act 1858 created the office of Secretary of State for India in 1858 to oversee the affairs of India, which was advised by a new Council of India with 15 members (based in London). The existing Council of Four was formally renamed as the Council of Governor General of India or Executive Council of India. The Council of India was later abolished by Government of India Act 1935.
Following the adoption of the Government of India Act of 1858, the Governor-General as representing the Crown became known as the Viceroy. The designation 'Viceroy', although it was most frequently used in ordinary parlance, had no statutory authority, and was never employed by Parliament. Although the Proclamation of 1858 announcing the assumption of the government of India by the Crown referred to Lord Canning as "first Viceroy and Governor-General", none of the Warrants appointing his successors referred to them as 'Viceroys', and the title, which was frequently used in Warrants dealing with precedence and in public notifications, was basically one of ceremony used in connection with the state and social functions of the Sovereign's representative. The Governor-General continued to be the sole representative of the Crown, and the Government of India continued to be vested in the Governor-General-in-Council.[1]
From the year 1858 onwards, the appointments of Governor-General of India were made by British Crown at the advice of Secretary of State for India. The office of Governor-General continued to exist as a ceremonial post in each of the new dominions until they adopted republican constitutions in 1950 and 1956 respectively..
List of Viceroys and Governor-Generals[edit]
Name
(Birth–Death)
Portrait
Term of office
Happenings
Appointer
Governors of the Presidency of Fort William (Bengal), 1773–1833
Warren Hastings
(1732-1818) 20 October 1773
(originally joined on 28 April 1772) 1 February 1785
Regulating Act of 1773
Supreme Council Of Bengal
Asiatic Society of Bengal
Stopped Mughal pension to Shah Alam II
Stopped Diarchy in Bengal
New Sanskrit School by Jonathan Deccan
Moved Treasury from Murshidabad to Calcutta
Bengal Gazette- First Indian newspaper published
First Anglo-Maratha War (1775–82)
Second Anglo-Mysore war (1780–84)
First Rohilla War of 1773–1774
Second Rohilla War 1779
Experimentation on land settlements. (1772-five years settlement, changed to 1 year in 1776)
English Translation of Bhagwat Gita[2]
East India
Company

(1773–1858)
John Macpherson
(acting)
(1745–1821) 1 February 1785 12 September 1786
Charles Cornwallis, The Earl Cornwallis[3]
(1738–1805) 12 September 1786 28 October 1793
Established lower courts and appellate courts
Permanent Settlement in Bihar and Bengal in 1793
3rd Mysore war
Introduction of Cornwallis Code
Introduction of Civil Services in India
John Shore
(1751–1834) 28 October 1793 18 March 1798
Policy of Non-intervention
Charter Act of 1793
Battle of Kharda between Nizam and Marathas (1795)
Alured Clarke
(acting)
(1744–1832) 18 March 1793 18 May 1798
Richard Wellesley, Earl of Mornington [4]
(1760–1842) 18 May 1798 30 July 1805
Introduction of Subsidiary Alliance
Fourth Anglo Mysore War 1799
Second Anglo-Maratha War (1803–05)
Fort William College at Calcutta
Formation of Madras Presidency in 1801
The Marquess Cornwallis
(1738–1805) 30 July 1805 5 October 1805
George Barlow, Bt
(acting)
(1762–1847) 10 October 1805 31 July 1807
Sepoy mutiny at Vellore
The Lord Minto
(1751–1814) 31 July 1807 4 October 1813
Charter Act of 1813
Treaty of Amritsar, 1809 with Ranjit Singh
Francis Rawdon-Hastings, The Earl of Moira
[5]
(1754–1826)
4 October 1813 9 January 1823
Ended the policy of Non-intervention
Third Anglo-Maratha War (1816-1818)
1816,Treaty of Sagauli with Nepal
Creation of Bombay Presidency in 1818
Establishment of Ryotwari System in Madras and
Mahalwari System in Central India,Punjab And Western UP.
John Adam
(acting)
(1779–1825) 9 January 1823 1 August 1823
The Lord Amherst[6]
(1773–1857) 1 August 1823 13 March 1828
First Anglo-Burmese War (1824–26)
Treaty of Yandabo, 1826
William Butterworth Bayley
(acting)
(1782–1860) 13 March 1828 4 July 1828
Governors-General of India, 1833–1858
Lord William Bentinck
(1774–1839) 4 July 1828 20 March 1835
First Governor General of India
Bengal Sati Regulation, 1829
Charter Act, 1833
Saint Helena Act 1833
English Education Act 1835
Medical College and Hospital, Kolkata
East India
Company

(1773–1858)
Charles Metcalfe, Bt
(acting)
(1785–1846) 20 March 1835 4 March 1836
The Lord Auckland[7]
(1784–1849) 4 March 1836 28 February 1842
The Lord Ellenborough
(1790–1871) 28 February 1842 June 1844
William Wilberforce Bird
(acting)
(1784–1857) June 1844 23 July 1844
Henry Hardinge[8]
(1785–1856) 23 July 1844 12 January 1848
The First Anglo-Sikh War (1845–46)
The Second Anglo-Sikh War (1848–49)
Treaty of Lahore
The Earl of Dalhousie[9]
(1812–1860) 12 January 1848 28 February 1856
Doctrine of Lapse
Charles Wood Despatch
Second Anglo-Burmese War (1852)
First Railway line connecting Bombay and Thane
First telegraph Line was laid between Diamond Harbour to Calcutta.
Post Office Act, 1854
Hindu Widows' Remarriage Act, 1856
Established Public Works Department
Engineering College was established at Roorkee
Governors-General and Viceroys of India, 1858–1947
The Viscount Canning[10]
(1812–1862) 28 February 1856 21 March 1862
University of Bombay, Calcutta and Madras were set up in 1858
The revolt of 1857
The Government of India Act, 1858
Hindu Widows' Remarriage Act, 1856
Victoria

(1858–1901)
The Earl of Elgin
(1811–1863) 21 March 1862 20 November 1863
Robert Napier
(acting)
(1810–1890) 21 November 1863 2 December 1863
William Denison
(acting)
(1804–1871) 2 December 1863 12 January 1864
Sir John Lawrence, Bt
(1811–1879) 12 January 1864 12 January 1869
Bhutan War
Establishment of Shimla as India's summer capital in 1863
The Earl of Mayo
(1822–1872) 12 January 1869 8 February 1872
Assassinated by a Pathan Sher Ali Afridi
Sir John Strachey
(acting)
(1823–1907) 9 February 1872 23 February 1872
The Lord Napier
(acting)
(1819–1898) 24 February 1872 3 May 1872
The Lord Northbrook
(1826–1904) 3 May 1872 12 April 1876
The Lord Lytton
(1831–1891) 12 April 1876 8 June 1880
Vernacular Press Act
Second Anglo-Afghan War
The Indians Act (1878)[11]
The Marquess of Ripon
(1827–1909) 8 June 1880 13 December 1884
First Factory Act (1881)
Repeal of the Vernacular Press Act (1882)
Ilbert Bill
The Earl of Dufferin
(1826–1902) 13 December 1884 10 December 1888
Formation of Indian National Congress
Third Anglo-Burmese War
The Marquess of Lansdowne
(1845–1927) 10 December 1888 11 October 1894
The Earl of Elgin
(1849–1917) 11 October 1894 6 January 1899
The Lord Curzon of Kedleston[12]
(1859–1925) 6 January 1899 18 November 1905
Partition of Bengal (1905)
Second Delhi Darbar (1903)
The Earl of Minto
(1845–1914) 18 November 1905 23 November 1911 Edward VII

(1901–1910)
The Lord Hardinge of Penshurst
(1858–1944) 23 November 1911 4 April 1916 George V

(1910–1936)
The Lord Chelmsford
(1868–1933) 4 April 1916 2 April 1921
The Earl of Reading
(1860–1935) 2 April 1921 3 April 1926
The Lord Irwin
(1881–1959) 3 April 1926 18 April 1931
The Earl of Willingdon
(1866–1941) 18 April 1931 18 April 1936
The Marquess of Linlithgow
(1887–1952) 18 April 1936 1 October 1943 Edward VIII

(1936)
The Viscount Wavell
(1883–1950) 1 October 1943 21 February 1947 George VI

(1936–1950)
The Viscount Mountbatten of Burma
(1900–1979) 21 February 1947 15 August 1947
Governors-General of the Dominion of India, 1947–1950
The Viscount Mountbatten of Burma[13]
(1900–1979) 15 August 1947 21 June 1948 George VI

(1936–1950)
C. Rajagopalachari
(1878–1972) 21 June 1948 26 January 1950
See also[edit]
Council of India
Secretary of State for India
List of Presidents of India
Footnotes[edit]
Jump up ^ Imperial Gazetteer of India, Clarendon Press, Oxford, New Edition 1909, vol 4, p. 16.
Jump up ^ Clarke, John James (1 January 1997). Oriental Enlightenment: The Encounter Between Asian and Western Thought. Psychology Press. ISBN 9780415133753.
Jump up ^ Created Marquess Cornwallis in 1792.
Jump up ^ Created Marquess Wellesley in 1799.
Jump up ^ Created Marquess of Hastings in 1816
Jump up ^ Created Earl Amherst in 1826.
Jump up ^ Created Earl of Auckland in 1839.
Jump up ^ Created Viscount Hardinge in 1846.
Jump up ^ Created Marquess of Dalhousie in 1849.
Jump up ^ Created Earl Canning in 1859.
Jump up ^ "What was the Arms Act 1878? - Quora". www.quora.com. Retrieved 2017-01-13.
Jump up ^ The Lord Ampthill was acting Governor-General in 1904
Jump up ^ Created Earl Mountbatten of Burma on 28 October 1947.
[hide]
v
t
e
Governors-General of India
1774–1833
[Governors of the Presidency of
Fort William (Bengal)]
Warren Hastings
Sir John Macpherson
Charles Cornwallis
John Shore
Alured Clarke
Richard Wellesley
Charles Cornwallis
Sir George Barlow
Gilbert Elliot-Murray-Kynynmound
Francis Rawdon-Hastings
John Adam
William Amherst
William Butterworth Bayley
Lord William Bentinck
. .
1833–1858
[Governors-General of India]
Lord William Bentinck (continued)
Charles Metcalfe
George Eden
Edward Law
William Wilberforce Bird
Henry Hardinge
James Broun-Ramsay Dalhousie
Charles Canning
1858–1947
[Governors-General and Viceroys of India]
Charles Canning (continued)
James Bruce
Robert Napier
William Denison
John Lawrence
Richard Bourke
John Strachey
Francis Napier
Thomas Baring
Robert Bulwer-Lytton
George Robinson
Frederick Hamilton-Temple-Blackwood
Henry Petty-Fitzmaurice
Victor Bruce
George Curzon
Gilbert Elliot-Murray-Kynynmound
Charles Hardinge
Frederic Thesiger
Rufus Isaacs
Edward Wood
Freeman Freeman-Thomas
Victor Hope
Archibald Wavell
Louis Mountbatten
1947–1950
[Governors-General of the Union of India]
Louis Mountbatten (continued)
C. Rajagopalachari
Retrieved from "https://en.wikipedia.org/w/index.php?title=List_of_governors-general_of_India&oldid=799236304"
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The Incident (Modern Family)
From Wikipedia, the free encyclopedia
Jump to: navigation, search
"The Incident"
Modern Family episode
Episode no.
Season 1
Episode 4
Directed by
Jason Winer
Written by
Steven Levitan
Production code
1ARG05
Original air date
October 14, 2009
Guest appearance(s)
Shelley Long (DeDe Pritchett)
Reid Ewing (Dylan)
Episode chronology
← Previous
"Come Fly with Me" Next →
"Coal Digger"
List of Modern Family episodes
"The Incident" is the fourth episode of the American family sitcom Modern Family. It premiered on American Broadcasting Company in the United States on October 14, 2009. The episode was written by co-creator and executive producer of Modern Family, Steven Levitan and directed by series main director, Jason Winer.[1]
In the episode, Mitchell and Claire's mother visit the family months after she embarrassed Jay in front of his family on his wedding day. Phil and Claire try to judge whether Haley's boyfriend is acceptable to take her to a concert or not. Mitchell goes to his father's house to tell him that they are inviting his ex-wife to a party, but he does not tell Gloria. At the reunion, Gloria and Jay's ex-wife begin to fight, but Haley's boyfriend stops them.
"The Incident" received positive reviews from the television critics and received a 3.6/10 in the 18-49 demographic according to Nielsen Media Research.
Contents
[hide]
1 Plot
2 Production
3 Reception
3.1 Ratings
3.2 Reviews
4 References
5 External links
Plot[edit]
DeDe (Shelley Long), Claire (Julie Bowen) and Mitchell's (Jesse Tyler Ferguson) mother, stops in for a surprise visit and wants to try to make up with everyone that she offended during "the incident" that she caused at Jay (Ed O'Neill) and Gloria's (Sofía Vergara) wedding. DeDe had become progressively drunk during the event, eventually having to be carried out, making a scene. In the meantime, Haley (Sarah Hyland) wants to attend a concert with her boyfriend, Dylan (Reid Ewing), but Claire and Phil (Ty Burrell) refuse to let her go.
Later, everything comes into play when the whole family meets at Claire's house for dinner. Dylan sings a song to prove that he is good enough to take Haley to the concert. While the song still sounds appropriate Phil and Claire are considering to let her go with him at the concert. When though Dylan starts singing the words "I just wanna do you, do you. Do you wanna do me, do me" and the parents see that Haley is smiling, they change their minds. The next morning everyone sings Dylan's song to themselves.
Production[edit]
The episode "The Incident" was written by Steven Levitan and directed by Jason Winer. The episode is Steven Levitan's third writing credit after the "Pilot" and "The Bicycle Thief".[2] The episode is also Jason Winer's fourth directors credit after "Pilot", "The Bicycle Thief" and "Come Fly with Me.[3][4] It is also Shelley Long's first guest appearance on the show as Jay's Ex-wife and Mitchell & Claire's mother.[5]
Reception[edit]
Ratings[edit]
In its original American broadcast, "The Incident" was viewed by an estimated 9.353 million households and a 5.8 rating/9% share Nielsen rating receiving a 3.6 rating/10% share in the 18-49 demographic coming second in its timeslot after Criminal Minds.[6]
Reviews[edit]
"The Incident" was widely acclaimed by television critics. BuddyTV named this episode the 33rd best episode of 2009 saying "The matriarch of the family returned to cause trouble, but what sealed the deal on this episode's greatness was Dylan's performance of his catchy but wildly inappropriate song "In the Moonlight"[7] Robert Canning of IGN gave the episode a 9.0/10 saying it was "Outstanding" and ""The Incident" was a great episode with a far more personal storyline than generically learning responsibility with a new bike." referring to "The Bicycle Thief," the second episode of the first season"[8]
Jason Hughes of TV Squad gave the episode a positive review and praised the casting of Shelley Long saying "She hasn't lost even a bit of her comic chops, either. I'm hoping she agreed to come back and play this role over and over again."[9]
Donna Bowman of The A.V. Club gave the episode an A saying "Last week I wrote that Modern Family's sense of sky's-the-limit potential arises "not because it's starting from somewhere and going somewhere else -- but because it's got so much territory to explore right where it's plopped itself down." And this week it's like they've set out to prove me right."[10]
Michael Slezak of Entertainment Weekly remarked that "Just when you think Modern Family can’t get any funnier, it does. And I say that both about the series generally and last night’s episode specifically. "[11]
James Poniewozik of Time stated "With the arrival of Mom (Shelley Long) providing the excuse (of Claire and Mitchell acting as brother and sister), this episode added some layers to their family dynamic (though we had already heard that Claire had been a rebellious daughter)."[12]
Many critics have praised the casting of Shelley Long as DeDe. Michael Slezak said "The casting of Shelley Long as Jay’s passive-aggressive (except when violently aggressive) yet sexually reactivated ex-wife was a stroke of genius."[11] Robert Canning said "Long was a great choice for this sweet but aggravating character that could be as free-spirited as she was mean.".[8]
References[edit]
Jump up ^ "Modern Family: The Incident Writers, Directors, and Crew". TV.com. Retrieved 2009-12-14.
Jump up ^ "Steven Levitan (I)-Filmography by TV series". Internet Movie Database. Retrieved 2010-08-09.
Jump up ^ "Jason Winer - Filmography by TV series". Internet Movie Database. Retrieved 2010-08-09.
Jump up ^ "Modern Family-The Incident". Yahoo TV. Retrieved 2010-08-09.
Jump up ^ "Shelley Long (I)". Internet Movie Database. Retrieved 2010-08-09.
Jump up ^ Seidman, Robert (October 15, 2009). "Wednesday broadcast finals: Glee, The Middle, Jay Leno tick up, CBS comedies, Mercy, L&O:SVU tick down". TV by the Numbers. Retrieved June 6, 2010.
Jump up ^ "TV's 50 Best Episodes of 2009". Buddy TV.
^ Jump up to: a b Canning, Robert (October 15, 2009). "Modern Family: "The Incident" Review". IGN. Retrieved June 3, 2010.
Jump up ^ Hughes, Jason (October 15, 2009) http://www.tvsquad.com/2009/10/15/modern-family-the-incident/ Retrieved 2010-06-06 Archived January 29, 2010, at the Wayback Machine.
Jump up ^ Bowman, Donna (October 14, 2009). "Modern Family: "The Incident"". The A.V. Club. Retrieved June 6, 2010.
^ Jump up to: a b Slezak, Michael (October 15, 2009). "'Modern Family' recap: Warning -- Shelley Long is a choking hazard!". Entertainment Weekly. Retrieved June 11, 2010.
Jump up ^ Poniewozik, James (October 15, 2009). "Modern Family Watch: Mother's Day". Entertainment Time. Retrieved June 11, 2010.
External links[edit]
"The Incident" on IMDb
"The Incident" at TV.com
"The Incident" at ABC.com
[hide]
v
t
e
Modern Family
Awards and nominations
Characters
Episodes
1
2
3
4
5
6
7
8
Season 1
"Pilot"
"The Bicycle Thief"
"Come Fly with Me"
"The Incident"
"Coal Digger"
"Run for Your Wife"
"En Garde"
"Great Expectations"
"Fizbo"
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Sensitivity and specificity
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This article may be too technical for most readers to understand. Please help improve it to make it understandable to non-experts, without removing the technical details. The talk page may contain suggestions. (June 2017) (Learn how and when to remove this template message)
Sensitivity and specificity are statistical measures of the performance of a binary classification test, also known in statistics as classification function:
Sensitivity (also called the true positive rate, the recall, or probability of detection[1] in some fields) measures the proportion of positives that are correctly identified as such (e.g. the percentage of sick people who are correctly identified as having the condition).
Specificity (also called the true negative rate) measures the proportion of negatives that are correctly identified as such (e.g. the percentage of healthy people who are correctly identified as not having the condition).
Another way to understand in the context of medical tests is that sensitivity is the extent to which true positives are not missed/overlooked (so false negatives are few) and specificity is the extent to which positives really represent the condition of interest and not some other condition being mistaken for it (so false positives are few). Thus a highly sensitive test rarely overlooks a positive (for example, showing "nothing bad" despite something bad existing); a highly specific test rarely registers a positive for anything that is not the target of testing (for example, finding one bacterial species when another closely related one is the true target); and a test that is highly sensitive and highly specific does both, so it "rarely overlooks a thing that it is looking for" and it "rarely mistakes anything else for that thing." Because most medical tests do not have sensitivity and specificity values above 99%, "rarely" does not equate to certainty. But for practical reasons, tests with sensitivity and specificity values above 90% have high credibility, albeit usually no certainty, in differential diagnosis.
Sensitivity therefore quantifies the avoiding of false negatives, and specificity does the same for false positives. For any test, there is usually a trade-off between the measures – for instance, in airport security since testing of passengers is for potential threats to safety, scanners may be set to trigger alarms on low-risk items like belt buckles and keys (low specificity), in order to increase the probability of identifying dangerous objects and minimize the risk of missing objects that do pose a threat (high sensitivity). This trade-off can be represented graphically using a receiver operating characteristic curve. A perfect predictor would be described as 100% sensitive, meaning all sick individuals are correctly identified as sick, and 100% specific, meaning no healthy individuals are incorrectly identified as sick. In reality, however, any non-deterministic predictor will possess a minimum error bound known as the Bayes error rate.
Contents
[hide]
1 Definitions
1.1 Application to screening study
1.2 Confusion matrix
2 Sensitivity
3 Specificity
4 Graphical illustration
5 Medical examples
5.1 Misconceptions
5.2 Sensitivity index
6 Worked example
7 Estimation of errors in quoted sensitivity or specificity
8 Terminology in information retrieval
9 See also
10 References
11 Further reading
12 External links
Definitions[edit]
Terminology and derivations
from a confusion matrix
(number of) positive samples (P)
(number of) negative samples (N)
(number of) true positive (TP)
eqv. with hit
(number of) true negative (TN)
eqv. with correct rejection
(number of) false positive (FP)
eqv. with false alarm, Type I error
(number of) false negative (FN)
eqv. with miss, Type II error
sensitivity or true positive rate (TPR)
eqv. with hit rate, recall
T P R = T P / P = T P / ( T P + F N ) {\displaystyle {\mathit {TPR}}={\mathit {TP}}/P={\mathit {TP}}/({\mathit {TP}}+{\mathit {FN}})}
specificity (SPC) or true negative rate
S P C = T N / N = T N / ( T N + F P ) {\displaystyle {\mathit {SPC}}={\mathit {TN}}/N={\mathit {TN}}/({\mathit {TN}}+{\mathit {FP}})}
precision or positive predictive value (PPV)
P P V = T P / ( T P + F P ) {\displaystyle {\mathit {PPV}}={\mathit {TP}}/({\mathit {TP}}+{\mathit {FP}})}
negative predictive value (NPV)
N P V = T N / ( T N + F N ) {\displaystyle {\mathit {NPV}}={\mathit {TN}}/({\mathit {TN}}+{\mathit {FN}})}
fall-out or false positive rate (FPR)
F P R = F P / N = F P / ( F P + T N ) = 1 − S P C {\displaystyle {\mathit {FPR}}={\mathit {FP}}/N={\mathit {FP}}/({\mathit {FP}}+{\mathit {TN}})=1-{\mathit {SPC}}}
false negative rate (FNR)
F N R = F N / ( T P + F N ) = 1 − T P R {\displaystyle {\mathit {FNR}}={\mathit {FN}}/({\mathit {TP}}+{\mathit {FN}})=1-{\mathit {TPR}}}
false discovery rate (FDR)
F D R = F P / ( T P + F P ) = 1 − P P V {\displaystyle {\mathit {FDR}}={\mathit {FP}}/({\mathit {TP}}+{\mathit {FP}})=1-{\mathit {PPV}}}
accuracy (ACC)
A C C = ( T P + T N ) / ( T P + F P + F N + T N ) {\displaystyle {\mathit {ACC}}=({\mathit {TP}}+{\mathit {TN}})/({\mathit {TP}}+{\mathit {FP}}+{\mathit {FN}}+{\mathit {TN}})}
F1 score
is the harmonic mean of precision and sensitivity
F 1 = 2 T P / ( 2 T P + F P + F N ) {\displaystyle {\mathit {F1}}=2{\mathit {TP}}/(2{\mathit {TP}}+{\mathit {FP}}+{\mathit {FN}})}
Matthews correlation coefficient (MCC)
T P × T N − F P × F N ( T P + F P ) ( T P + F N ) ( T N + F P ) ( T N + F N ) {\displaystyle {\frac {{\mathit {TP}}\times {\mathit {TN}}-{\mathit {FP}}\times {\mathit {FN}}}{\sqrt {({\mathit {TP}}+{\mathit {FP}})({\mathit {TP}}+{\mathit {FN}})({\mathit {TN}}+{\mathit {FP}})({\mathit {TN}}+{\mathit {FN}})}}}}
Informedness
T P R + S P C − 1 {\displaystyle {\mathit {TPR}}+{\mathit {SPC}}-1}
Markedness
P P V + N P V − 1 {\displaystyle {\mathit {PPV}}+{\mathit {NPV}}-1}
Sources: Fawcett (2006) and Powers (2011).[2][3]
Application to screening study[edit]
Imagine a study evaluating a new test that screens people for a disease. Each person taking the test either has or does not have the disease. The test outcome can be positive (classifying the person as having the disease) or negative (classifying the person as not having the disease). The test results for each subject may or may not match the subject's actual status. In that setting:
True positive: Sick people correctly identified as sick
False positive: Healthy people incorrectly identified as sick
True negative: Healthy people correctly identified as healthy
False negative: Sick people incorrectly identified as healthy
In general, Positive = identified and negative = rejected. Therefore:
True positive = correctly identified
False positive = incorrectly identified
True negative = correctly rejected
False negative = incorrectly rejected
Confusion matrix[edit]
Let us consider a group with P positive instances and N negative instances of some condition. The four outcomes can be formulated in a 2×2 contingency table or confusion matrix, as follows:

True condition
Total population Condition positive Condition negative Prevalence = Σ Condition positive/Σ Total population Accuracy (ACC) = Σ True positive + Σ True negative/Σ Total population
Predicted
condition Predicted condition
positive True positive False positive,
Type I error Positive predictive value (PPV), Precision = Σ True positive/Σ Predicted condition positive False discovery rate (FDR), probability of false alarm = Σ False positive/Σ Predicted condition positive
Predicted condition
negative False negative,
Type II error True negative False omission rate (FOR) = Σ False negative/Σ Predicted condition negative Negative predictive value (NPV) = Σ True negative/Σ Predicted condition negative
Click thumbnail for interactive chart:
True positive rate (TPR), Recall, Sensitivity, probability of detection = Σ True positive/Σ Condition positive False positive rate (FPR), Fall-out= Σ False positive/Σ Condition negative Positive likelihood ratio (LR+) = TPR/FPR Diagnostic odds ratio (DOR) = LR+/LR− F1 score = 2/1/Recall + 1/Precision
False negative rate (FNR), Miss rate = Σ False negative/Σ Condition positive True negative rate (TNR), Specificity (SPC) = Σ True negative/Σ Condition negative Negative likelihood ratio (LR−) = FNR/TNR
Sensitivity[edit]
Sensitivity refers to the test's ability to correctly detect ill patients who do have the condition.[4] In the example of a medical test used to identify a disease, the sensitivity of the test is the proportion of people who test positive for the disease among those who have the disease. Mathematically, this can be expressed as:
sensitivity = number of true positives number of true positives + number of false negatives = number of true positives total number of sick individuals in population = probability of a positive test given that the patient has the disease {\displaystyle {\begin{aligned}{\text{sensitivity}}&={\frac {\text{number of true positives}}{{\text{number of true positives}}+{\text{number of false negatives}}}}\\[8pt]&={\frac {\text{number of true positives}}{\text{total number of sick individuals in population}}}\\[8pt]&={\text{probability of a positive test given that the patient has the disease}}\end{aligned}}}
A negative result in a test with high sensitivity is useful for ruling out disease.[4] A high sensitivity test is reliable when its result is negative, since it rarely misdiagnoses those who have the disease. A test with 100% sensitivity will recognize all patients with the disease by testing positive. A negative test result would definitively rule out presence of the disease in a patient.
A positive result in a test with high sensitivity is not useful for ruling in disease. Suppose a 'bogus' test kit is designed to show only one reading, positive. When used on diseased patients, all patients test positive, giving the test 100% sensitivity. However, sensitivity by definition does not take into account false positives. The bogus test also returns positive on all healthy patients, giving it a false positive rate of 100%, rendering it useless for detecting or "ruling in" the disease.
Sensitivity is not the same as the precision or positive predictive value (ratio of true positives to combined true and false positives), which is as much a statement about the proportion of actual positives in the population being tested as it is about the test.
The calculation of sensitivity does not take into account indeterminate test results. If a test cannot be repeated, indeterminate samples either should be excluded from the analysis (the number of exclusions should be stated when quoting sensitivity) or can be treated as false negatives (which gives the worst-case value for sensitivity and may therefore underestimate it).
Specificity[edit]
Specificity relates to the test's ability to correctly reject healthy patients without a condition. Consider the example of a medical test for diagnosing a disease. Specificity of a test is the proportion of healthy patients known not to have the disease, who will test negative for it. Mathematically, this can also be written as:
specificity = number of true negatives number of true negatives + number of false positives = number of true negatives total number of well individuals in population = probability of a negative test given that the patient is well {\displaystyle {\begin{aligned}{\text{specificity}}&={\frac {\text{number of true negatives}}{{\text{number of true negatives}}+{\text{number of false positives}}}}\\[8pt]&={\frac {\text{number of true negatives}}{\text{total number of well individuals in population}}}\\[8pt]&={\text{probability of a negative test given that the patient is well}}\end{aligned}}}
A positive result in a test with high specificity is useful for ruling in disease. The test rarely gives positive results in healthy patients. A test with 100% specificity will read negative, and accurately exclude disease from all healthy patients. A positive result signifies a high probability of the presence of disease.[5]
A test with a higher specificity has a lower type I error rate.
Graphical illustration[edit]
High sensitivity and low specificity
Low sensitivity and high specificity
Medical examples[edit]
In medical diagnosis, test sensitivity is the ability of a test to correctly identify those with the disease (true positive rate), whereas test specificity is the ability of the test to correctly identify those without the disease (true negative rate). If 100 patients known to have a disease were tested, and 43 test positive, then the test has 43% sensitivity. If 100 with no disease are tested and 96 return a negative result, then the test has 96% specificity. Sensitivity and specificity are prevalence-independent test characteristics, as their values are intrinsic to the test and do not depend on the disease prevalence in the population of interest.[6] Positive and negative predictive values, but not sensitivity or specificity, are values influenced by the prevalence of disease in the population that is being tested. These concepts are illustrated graphically in this applet Bayesian clinical diagnostic model which show the positive and negative predictive values as a function of the prevalence, the sensitivity and specificity.
Misconceptions[edit]
It is often claimed that a highly specific test is effective at ruling in a disease when positive, while a highly sensitive test is deemed effective at ruling out a disease when negative.[7][8] This has led to the widely used mnemonics SPIN and SNOUT, according to which a highly SPecific test, when Positive, rules IN disease (SP-P-IN), and a highly 'SeNsitive' test, when Negative rules OUT disease (SN-N-OUT). Both rules of thumb are, however, inferentially misleading, as the diagnostic power of any test is determined by both its sensitivity and its specificity.[9][10][11]
The tradeoff between Specificity and Sensitivity is explored in ROC analysis as a trade off between TPR and FPR (that is Recall and Fallout).[2] Giving them equal weight optimizes Informedness = Specificity+Sensitivity-1 = TPR-FPR, the magnitude of which gives the probability of an informed decision between the two classes (>0 represents appropriate use of information, 0 represents chance-level performance, <0 represents perverse use of information).[3]
Sensitivity index[edit]
The sensitivity index or d' (pronounced 'dee-prime') is a statistic used in signal detection theory. It provides the separation between the means of the signal and the noise distributions, compared against the standard deviation of the noise distribution. For normally distributed signal and noise with mean and standard deviations μ S {\displaystyle \mu _{S}} and σ S {\displaystyle \sigma _{S}} , and μ N {\displaystyle \mu _{N}} and σ N {\displaystyle \sigma _{N}} , respectively, d' is defined as:
d ′ = μ S − μ N 1 2 ( σ S 2 + σ N 2 ) {\displaystyle d'={\frac {\mu _{S}-\mu _{N}}{\sqrt {{\frac {1}{2}}(\sigma _{S}^{2}+\sigma _{N}^{2})}}}} [12]
An estimate of d' can be also found from measurements of the hit rate and false-alarm rate. It is calculated as:
d' = Z(hit rate) – Z(false alarm rate),[13]
where function Z(p), p ∈ [0,1], is the inverse of the cumulative Gaussian distribution.
d' is a dimensionless statistic. A higher d' indicates that the signal can be more readily detected.
Worked example[edit]
view
talk
edit
A worked example
A diagnostic test with sensitivity 67% and specificity 91% is applied to 2030 people to look for a disorder with a population prevalence of 1.48%
Patients with bowel cancer
(as confirmed on endoscopy)
Condition positive Condition negative
Fecal
occult
blood
screen
test
outcome Test
outcome
positive True positive
(TP) = 20 False positive
(FP) = 180 Positive predictive value
= TP / (TP + FP)
= 20 / (20 + 180)
= 10%
Test
outcome
negative False negative
(FN) = 10 True negative
(TN) = 1820 Negative predictive value
= TN / (FN + TN)
= 1820 / (10 + 1820)
≈ 99.5%
Sensitivity
= TP / (TP + FN)
= 20 / (20 + 10)
≈ 67%
Specificity
= TN / (FP + TN)
= 1820 / (180 + 1820)
= 91%
Related calculations
False positive rate (α) = type I error = 1 − specificity = FP / (FP + TN) = 180 / (180 + 1820) = 9%
False negative rate (β) = type II error = 1 − sensitivity = FN / (TP + FN) = 10 / (20 + 10) = 33%
Power = sensitivity = 1 − β
Likelihood ratio positive = sensitivity / (1 − specificity) = 0.67 / (1 − 0.91) = 7.4
Likelihood ratio negative = (1 − sensitivity) / specificity = (1 − 0.67) / 0.91 = 0.37
Hence with large numbers of false positives and few false negatives, a positive screen test is in itself poor at confirming the disorder (PPV = 10%) and further investigations must be undertaken; it did, however, correctly identify 66.7% of all cases (the sensitivity). However as a screening test, a negative result is very good at reassuring that a patient does not have the disorder (NPV = 99.5%) and at this initial screen correctly identifies 91% of those who do not have cancer (the specificity).
Estimation of errors in quoted sensitivity or specificity[edit]
Sensitivity and specificity values alone may be highly misleading. The 'worst-case' sensitivity or specificity must be calculated in order to avoid reliance on experiments with few results. For example, a particular test may easily show 100% sensitivity if tested against the gold standard four times, but a single additional test against the gold standard that gave a poor result would imply a sensitivity of only 80%. A common way to do this is to state the binomial proportion confidence interval, often calculated using a Wilson score interval.
Confidence intervals for sensitivity and specificity can be calculated, giving the range of values within which the correct value lies at a given confidence level (e.g., 95%).[14]
Terminology in information retrieval[edit]
In information retrieval, the positive predictive value is called precision, and sensitivity is called recall. Unlike the Specificity vs Sensitivity tradeoff, these measures are both independent of the number of true negatives, which is generally unknown and much larger than the actual numbers of relevant and retrieved documents. This assumption of very large numbers of true negatives versus positives is rare in other applications.[3]
The F-score can be used as a single measure of performance of the test for the positive class. The F-score is the harmonic mean of precision and recall:
F = 2 × precision × recall precision + recall {\displaystyle F=2\times {\frac {{\text{precision}}\times {\text{recall}}}{{\text{precision}}+{\text{recall}}}}}
In the traditional language of statistical hypothesis testing, the sensitivity of a test is called the statistical power of the test, although the word power in that context has a more general usage that is not applicable in the present context. A sensitive test will have fewer Type II errors.
See also[edit]
Science portal
Biology portal
Medicine portal
Brier score
Cumulative accuracy profile
False positive paradox
NCSS (statistical software) includes sensitivity and specificity analysis.
OpenEpi software program
Discrimination
Precision and recall
Statistical significance
Uncertainty coefficient, also called proficiency
Youden's J statistic
References[edit]
Jump up ^ "Detector Performance Analysis Using ROC Curves – MATLAB & Simulink Example". www.mathworks.com. Retrieved 11 August 2016.
^ Jump up to: a b Fawcett, Tom (2006). "An Introduction to ROC Analysis". Pattern Recognition Letters. 27 (8): 861–874. doi:10.1016/j.patrec.2005.10.010.
^ Jump up to: a b c Powers, David M W (2011). "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation" (PDF). Journal of Machine Learning Technologies. 2 (1): 37–63.
^ Jump up to: a b Altman, D. G.; Bland, J. M. (11 June 1994). "Statistics Notes: Diagnostic tests 1: sensitivity and specificity". BMJ. 308 (6943): 1552. PMC 2540489  . PMID 8019315. doi:10.1136/bmj.308.6943.1552 – via www.bmj.com.
Jump up ^ "SpPins and SnNouts". Centre for Evidence Based Medicine (CEBM). Retrieved 26 December 2013.
Jump up ^ Mangrulkar, Rajesh. "Diagnostic Reasoning I and II". Retrieved 24 January 2012.
Jump up ^ "Evidence-Based Diagnosis". Michigan State University.
Jump up ^ "Sensitivity and Specificity". Emory University Medical School Evidence Based Medicine course.
Jump up ^ Baron, JA (Apr–Jun 1994). "Too bad it isn't true.....". Medical decision making : an international journal of the Society for Medical Decision Making. 14 (2): 107. PMID 8028462. doi:10.1177/0272989X9401400202.
Jump up ^ Boyko, EJ (Apr–Jun 1994). "Ruling out or ruling in disease with the most sensitive or specific diagnostic test: short cut or wrong turn?". Medical decision making : an international journal of the Society for Medical Decision Making. 14 (2): 175–179. PMID 8028470. doi:10.1177/0272989X9401400210.
Jump up ^ Pewsner, D; Battaglia, M; Minder, C; Marx, A; Bucher, HC; Egger, M (Jul 24, 2004). "Ruling a diagnosis in or out with "SpPIn" and "SnNOut": a note of caution". BMJ (Clinical research ed.). 329 (7459): 209–13. PMC 487735  . PMID 15271832. doi:10.1136/bmj.329.7459.209.
Jump up ^ Gale, SD; Perkel, DJ (Jan 20, 2010). "A basal ganglia pathway drives selective auditory responses in songbird dopaminergic neurons via disinhibition". The Journal of Neuroscience. 30 (3): 1027–1037. PMC 2824341  . PMID 20089911. doi:10.1523/JNEUROSCI.3585-09.2010.
Jump up ^ Macmillan, Neil A.; Creelman, C. Douglas (15 September 2004). Detection Theory: A User's Guide. Psychology Press. p. 7. ISBN 978-1-4106-1114-7.
Jump up ^ "Diagnostic test online calculator calculates sensitivity, specificity, likelihood ratios and predictive values from a 2x2 table – calculator of confidence intervals for predictive parameters". medcalc.org.
Further reading[edit]
Altman DG, Bland JM (1994). "Diagnostic tests. 1: Sensitivity and specificity". BMJ. 308 (6943): 1552. PMC 2540489  . PMID 8019315. doi:10.1136/bmj.308.6943.1552.
Loong T (2003). "Understanding sensitivity and specificity with the right side of the brain". BMJ. 327 (7417): 716–719. PMC 200804  . PMID 14512479. doi:10.1136/bmj.327.7417.716.
External links[edit]
UIC Calculator
Vassar College's Sensitivity/Specificity Calculator
MedCalc Free Online Calculator
Bayesian clinical diagnostic model applet
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Non-deterministic algorithm

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Type I error

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Type II error

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Sensitivity (test)

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Specificity (tests)

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Positive predictive value

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False negative rate

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Precision (information retrieval)

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True positive rate

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Recall (information retrieval)

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Sensitivity (tests)

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Positive likelihood ratio

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True negative rate

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Bowel cancer

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Confidence intervals

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PubMed Identifier

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Academic clinical trials

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Blind experiment

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Epidemiological methods

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Point prevalence

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Period prevalence

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Association (statistics)

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Specificity and sensitivity

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List of clinical research topics

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values: حساسية ونوعية – Arabic

values: Sensibilidad y especificidad (estadística) – Spanish

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