text:
The Office Christmas specials
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"Christmas Special"
The Office episode
Directed by
Ricky Gervais
Stephen Merchant
Written by
Ricky Gervais
Stephen Merchant
Original air date
26 December 2003 (Part 1)
27 December 2003 (Part 2)
List of The Office episodes
The Office Christmas Specials are the two-part final episode of the British mockumentary comedy television series The Office. The specials were commissioned after the series' creators, Ricky Gervais and Stephen Merchant, announced that they would not write a full third series of the show. The first 45-minute part was broadcast on BBC One on 26 December 2003, and the second 50-minute part was shown the following evening. The episodes are presented in the style of "revisited" documentaries common on British television,[1] in which popular "docusoaps" (e.g. Airport), are brought back for one-off specials several years after the series concluded. David Brent (Gervais), forcibly made redundant at the end of the second series, is now a travelling salesman of cleaning supplies. Tim Canterbury (Martin Freeman) and Gareth Keenan (Mackenzie Crook) are still working at the offices of Wernham Hogg, and former Wernham Hogg receptionist Dawn Tinsley (Lucy Davis) now lives in Florida, though is flown back to Britain by the documentary crew to reunite with her old colleagues.
The episodes won various awards, including two British Academy Television Awards, a Royal Television Society award, and a Television Critics Association (America) award. They were also nominated for a British Comedy Award and a Primetime Emmy Award.
Contents
[hide]
1 Plot
1.1 Part 1
1.2 Part 2
2 Production
3 Reception
4 Home media
5 References
6 External links
Plot[edit]
Part 1[edit]
Three years after the documentary that made them famous, a different documentary crew revisits the office to see what the staff members are doing now. After being made redundant from Wernham Hogg, David Brent (Ricky Gervais) is now a travelling salesman who, following a failed single release of a cover of "If You Don't Know Me By Now," leeches off what little fame he has in a series of demeaning nightclub appearances alongside a crowd of minor celebrities. Dawn Tinsley (Lucy Davis) lives in Florida, trapped in a miserable existence with fiancé Lee (Joel Beckett), who is bouncing from job to job as they live with his sister rent-free and hidden from immigration authorities. In the office, Gareth Keenan (Mackenzie Crook) is now the general manager and Tim Canterbury (Martin Freeman) is still trapped in a job he hates with an obnoxious, pregnant desk-mate Anne (Elizabeth Berrington). Despite leaving Wernham Hogg three years ago, Brent still visits the office to see the staff and "keep up the morale". This does not sit well with Gareth or David's former boss Neil Godwin (Patrick Baladi), and it appears to the viewer that Brent misses his old job. When the programme makers offer to fly Dawn and Lee back for the office Christmas party, along with the appearance of David Brent, the scene is set for a reunion.
Part 2[edit]
Brent tells Neil that he has a date for the Christmas party, when he really does not. With Gareth's help, he searches Internet romance websites for suitable women, and lines up three possible candidates for blind dates. He takes the first to dinner, but accidentally steers the conversation to breasts. He calls from his car to arrange a meeting with the second woman (voiced by Julia Davis[1]), who does not realise who Brent is, and mocks the manager from The Office documentary. Brent hangs up. On the third date, Brent immediately finds the woman unattractive and boring. Dawn returns to Britain and arrives at the office. She and Tim immediately recreate old times by winding up Gareth. Brent pushes Neil's patience too far when he brings his dog into the office to show to the staff. Neil bars Brent from the office and later stops anyone who does not work for Wernham Hogg from going to the Christmas dinner. At the party, Brent anxiously awaits his fourth blind date. When the woman, Carol, arrives, she and Brent hit it off straight away. Lee tells Dawn that it is time to leave. She and Tim say awkward goodbyes and she leaves with Lee.
In the taxi, Dawn opens her "Secret Santa" present: an oil painting set from Tim with the words, "Never give up" written next to Dawn's sketch of Tim, which she had made earlier in the day. Brent walks his date to her car and she gestures through the window for him to call her. Returning to the office, he is subject to yet another offensive comment from Chris Finch (Ralph Ineson) about his date, and Brent, in a moment of anger, and having finally been pushed too far with the insults, tells him bluntly, "why don't you fuck off". In the final scenes, Dawn returns to the office and kisses Tim, indicating that she has left Lee. The whole office staff gather for a group photo. Brent asks to have a group photo of just him and the "old gang". While David, Tim, Dawn, Gareth, and the rest of the office members gather round for the photo, David finally manages to make his colleagues laugh, with an impersonation of Frank Spencer.
Production[edit]
Gervais and Stephen Merchant had announced that they would not write a third series of The Office even after the overwhelming success of the second. In an interview published in Heat in May 2003, Gervais announced that he and Merchant had begun writing a two-part Christmas special. Merchant joked that it would be the "same jokes, but with tinsel and David Brent in a Santa outfit".[2]
The episode was filmed between August and September. Scenes set in Florida were filmed in Spain.[3] Anne, Tim's new desk-mate, was written to be an even more annoying character than Gareth.[1]
In November, a woman from Saffron Walden claimed to have been sent the scripts in the post by mistake. She announced to The Sun that she intended to sell them for "thousands" of pounds.[4] The Daily Mail purchased the scripts and revealed most of the plot in an article published four weeks before the broadcast.[3] In a BBC Radio 1 Newsbeat interview, Gervais asked for a newspaper or magazine to buy the scripts and return them to the BBC as "a little Christmas present to the nation".[5]
Reception[edit]
The second series of The Office averaged four million viewers per week when it was broadcast on BBC Two. To get as many viewers as possible for the Christmas specials, the BBC's director of television Jana Bennet allowed it to be moved to BBC One.[6] The ratings were deemed a success by the BBC; Part 1 received 7.17 million viewers and a 30.97% share of the audience, and Part 2 received 6.14 million viewers and a 25.66% share. Part 1 was the third most popular programme on Boxing Day for 16- to 34-year-olds.[7]
A "clerical error" at the BBC meant Gervais was initially omitted for consideration for a British Academy Television Award for Best Comedy Performance in 2004. Crook, Freeman and Davis were all long-listed for the award.[8] A last-minute reprieve allowed Gervais to be included on the list, and he went on to win the BAFTA for Best Comedy Performance. Freeman had also been shortlisted. The specials themselves won in the Best Situation Comedy category. The award was presented to Gervais, Merchant and producer Ash Atalla.[9] At the Royal Television Society Programming Awards, the specials won in the Situation Comedy and Comedy Drama category.[10] The RTS jury commented "The problem of writing a final episode of a much-loved comedy was fully resolved. Tim gets Dawn. Dawn gets her oil paints. Brent gets the girl and we are carried along with it all. Perfection!"[11] Gervais was also nominated in the Comedy Performance category.[10] The specials were nominated for Best TV Comedy at the British Comedy Awards,[12] but lost to Little Britain, though Gervais and Merchant won the Writer of the Year award.[13]
The specials were broadcast in the United States on 21 October 2004. BBC America merged both parts into a single 135-minute episode entitled The Office Special. The making-of documentary How I Made the Office, previously released on the Series 1 DVD, was broadcast after the special. In a preview in The Hollywood Reporter, Cynthia Littleton highlighted the scene where Brent tells Carol that the documentary crew edited his antics to make him look like a fool as being true to life, comparing it to the various real-life lawsuits filed by Survivor contestants against CBS. Littleton also commented positively on the scene where Gareth asks Brent to "call ahead", and Brent's music video.[14] In The New York Times, Alessandra Stanley wrote that the specials are "as wickedly, painfully funny as the first two seasons and, in tiny, fleeting doses, as delicately tender".[15]
The following year it was nominated for the Primetime Emmy Award for Outstanding Made for Television Movie.[16] Gervais and Merchant received a nomination for Outstanding Writing for a Miniseries, Movie or a Dramatic Special.[17] The episodes won in the Outstanding Achievement in Movies, Mini-series & Specials category at the American Television Critics Association Awards. The award was collected by Lucy Davis.[18]
The specials appeared at #4 on Channel 4's 100 Greatest Christmas Moments polls in 2004.[19]
Home media[edit]
The specials were released on VHS and DVD in the UK on 25 October 2004 by BBC Video. Extra features include an audio commentary on Part 2 by Merchant and Gervais, a retrospective documentary entitled The Office: Closed For Business, the full video for Brent's version of "If You Don't Know Me By Now", a featurette about the cast and crew collecting the Golden Globes, and a studio version of "Freelove Freeway"—Brent's song from Series 1, Episode 4.[20] It was released in the United States on 16 November 2004 with the same special features.[21]
References[edit]
^ Jump up to: a b c Gervais, Ricky; Stephen Merchant. (2003). Audio commentary for The Office Christmas Special Part 2 [DVD]. BBC Video.
Jump up ^ Staff (27 May 2003). "The Office back for Christmas". BBC News Online. Retrieved on 21 October 2008.
^ Jump up to: a b Nixson, Matt (1 December 2003). "The Office secrets revealed". Daily Mail (reprinted on Evening Standard website). Associated Newspapers. Retrieved on 21 October 2008.
Jump up ^ Staff (28 November 2003). "Office plot leak fear". The Sun. News Group Newspapers. Retrieved on 21 October 2008.
Jump up ^ Staff (28 November 2003). "Ricky pleads for lost scripts". Evening Standard. Associated Newspapers. Retrieved on 21 October 2008.
Jump up ^ Matthews, Sam (5 August 2003). "BBC1 gets The Office for Christmas". Broadcastnow. Emap Media. Retrieved on 21 October 2008.
Jump up ^ Reevell, Philip (16 January 2004). "The ghastly ghosts of Christmas TV presents". Broadcastnow. Emap Media. Retrieved on 21 October 2008.
Jump up ^ Deans, Jason (15 January 2004). "Gervais falls victim to BBC Bafta bungle". MediaGuardian. Guardian News and Media. Retrieved on 21 October 2008.
Jump up ^ "Past Winners and Nominations - Television 2004". British Academy of Film and Television Arts. Retrieved on 21 October 2008.
^ Jump up to: a b "RTS Programme Awards 2003 Archived 2009-03-22 at the Wayback Machine.". Royal Television Society. Retrieved on 21 October 2008.
Jump up ^ "Jury's comments". Royal Television Society. (archived from the original on 18 January 2005) Retrieved on 6 March 2009.
Jump up ^ Armstrong, Stephen (11 November 2004). "BBC laughs all the way to the comedy awards". The Guardian. Guardian News and Media. Retrieved on 21 October 2008.
Jump up ^ "Past Winners 2004". British Comedy Awards. Retrieved on 21 October 2008.
Jump up ^ Littleton, Cynthia (11 October 2004). "Mocku redux: 'Office' returns with epilogue" (subscription). The Hollywood Reporter. Neilsen Business Media. Retrieved on 21 October 2008.
Jump up ^ Stanley, Alessandra (21 October 2004). "More Slices of Office Life, No Laugh Track Required". The New York Times. The New York Times Company. Retrieved on 21 October 2008.
Jump up ^ "Outstanding Made For Television Movie". Academy of Television Arts & Sciences. Retrieved on 21 October 2008.
Jump up ^ "Outstanding Writing For A Miniseries, Movie Or A Dramatic Special". Academy of Television Arts & Sciences. Retrieved on 21 October 2008.
Jump up ^ "TCA Awards winners". Television Critics Association. Retrieved on 21 October 2008.
Jump up ^ "100 Greatest Christmas Moments". Channel4.com. Retrieved on 21 October 2008.
Jump up ^ Staff (20 November 2004). "The Office - Christmas Specials". DVD Times. Retrieved on 21 October 2008.
Jump up ^ "The Office - Special". TVShowsonDVD.com. Retrieved on 21 October 2008.
External links[edit]
The Office Christmas Specials at BBC Online
[show]
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Type I and type II errors
From Wikipedia, the free encyclopedia
Jump to: navigation, search
This article is about erroneous outcomes of statistical tests. For closely related concepts in binary classification and testing generally, see false positives and false negatives.
In statistical hypothesis testing, a type I error is the incorrect rejection of a true null hypothesis (also known as a "false positive" finding), while a type II error is incorrectly retaining a false null hypothesis (also known as a "false negative" finding).[1] More simply stated, a type I error is to falsely infer the existence of something that is not there, while a type II error is to falsely infer the absence of something that is.
Contents
[hide]
1 Definition
2 Statistical test theory
2.1 Type I error
2.2 Type II error
2.3 Table of error types
3 Examples
3.1 Example 1
3.2 Example 2
3.3 Example 3
3.4 Example 4
4 Etymology
5 Related terms
5.1 Null hypothesis
5.2 Statistical significance
6 Application domains
6.1 Inventory control
6.2 Computers
6.2.1 Computer security
6.2.2 Spam filtering
6.2.3 Malware
6.2.4 Optical character recognition
6.3 Security screening
6.4 Biometrics
6.5 Medicine
6.5.1 Medical screening
6.5.2 Medical testing
6.6 Paranormal investigation
7 See also
8 Notes
9 References
10 External links
Definition[edit]
In statistics, a null hypothesis is a statement that one seeks to nullify with evidence to the contrary. Most commonly it is a statement that the phenomenon being studied produces no effect or makes no difference. An example of a null hypothesis is the statement "This diet has no effect on people's weight." Usually, an experimenter frames a null hypothesis with the intent of rejecting it: that is, intending to run an experiment which produces data that shows that the phenomenon under study does make a difference.[2] In some cases there is a specific alternative hypothesis that is opposed to the null hypothesis, in other cases the alternative hypothesis is not explicitly stated, or is simply "the null hypothesis is false" – in either event, this is a binary judgment, but the interpretation differs and is a matter of significant dispute in statistics.
A type I error (or error of the first kind) is the incorrect rejection of a true null hypothesis. Usually a type I error leads one to conclude that a supposed effect or relationship exists when in fact it doesn't. Examples of type I errors include a test that shows a patient to have a disease when in fact the patient does not have the disease, a fire alarm going on indicating a fire when in fact there is no fire, or an experiment indicating that a medical treatment should cure a disease when in fact it does not.
A type II error (or error of the second kind) is the failure to reject a false null hypothesis. Examples of type II errors would be a blood test failing to detect the disease it was designed to detect, in a patient who really has the disease; a fire breaking out and the fire alarm does not ring; or a clinical trial of a medical treatment failing to show that the treatment works when really it does.[3]
In terms of false positives and false negatives, a positive result corresponds to rejecting the null hypothesis, while a negative result corresponds to failing to reject the null hypothesis; "false" means the conclusion drawn is incorrect. Thus a type I error is a false positive, and a type II error is a false negative.
When comparing two means, concluding the means were different when in reality they were not different would be a Type I error; concluding the means were not different when in reality they were different would be a Type II error. Various extensions have been suggested as "Type III errors", though none have wide use.
All statistical hypothesis tests have a probability of making type I and type II errors. For example, all blood tests for a disease will falsely detect the disease in some proportion of people who don't have it, and will fail to detect the disease in some proportion of people who do have it. A test's probability of making a type I error is denoted by α. A test's probability of making a type II error is denoted by β. These error rates are traded off against each other: for any given sample set, the effort to reduce one type of error generally results in increasing the other type of error. For a given test, the only way to reduce both error rates is to increase the sample size, and this may not be feasible.
These terms are also used in a more general way by social scientists and others to refer to flaws in reasoning.[4] This article is specifically devoted to the statistical meanings of those terms and the technical issues of the statistical errors that those terms describe.
Statistical test theory[edit]
In statistical test theory, the notion of statistical error is an integral part of hypothesis testing. The test requires an unambiguous statement of a null hypothesis, which usually corresponds to a default "state of nature", for example "this person is healthy", "this accused is not guilty" or "this product is not broken". An alternative hypothesis is the negation of null hypothesis, for example, "this person is not healthy", "this accused is guilty" or "this product is broken". The result of the test may be negative, relative to the null hypothesis (not healthy, guilty, broken) or positive (healthy, not guilty, not broken). If the result of the test corresponds with reality, then a correct decision has been made. However, if the result of the test does not correspond with reality, then an error has occurred. Due to the statistical nature of a test, the result is never, except in very rare cases, free of error. Two types of error are distinguished: type I error and type II error.
Type I error[edit]
A type I error occurs when the null hypothesis (H0) is true, but is rejected. It is asserting something that is absent, a false hit. A type I error may be likened to a so-called false positive (a result that indicates that a given condition is present when it actually is not present).
The type I error rate or significance level is the probability of rejecting the null hypothesis given that it is true.[5][6] It is denoted by the Greek letter α (alpha) and is also called the alpha level. Often, the significance level is set to 0.05 (5%), implying that it is acceptable to have a 5% probability of incorrectly rejecting the null hypothesis.[5]
Type II error[edit]
A type II error occurs when the null hypothesis is false, but erroneously fails to be rejected. It is failing to assert what is present, a miss. A type II error may be compared with a so-called false negative (where an actual 'hit' was disregarded by the test and seen as a 'miss') in a test checking for a single condition with a definitive result of true or false. A Type II error is committed when we fail to believe a true alternative hypothesis.[7] In terms of folk tales, an investigator may fail to see the wolf ("failing to raise an alarm"). Again, H0: no wolf.
The rate of the type II error is denoted by the Greek letter β (beta) and related to the power of a test (which equals 1−β).
Table of error types[edit]
Tabularised relations between truth/falseness of the null hypothesis and outcomes of the test:[2]
Table of error types
Null hypothesis (H0) is
True
False
Decision About Null Hypothesis (H0)
Reject
Type I error
(False Positive) Correct inference
(True Positive)
Fail to reject
Correct inference
(True Negative) Type II error
(False Negative)
Examples[edit]
Example 1[edit]
Hypothesis: "Adding water to toothpaste protects against cavities."
Null hypothesis (H0): "Adding water to toothpaste has no effect on cavities."
This null hypothesis is tested against experimental data with a view to nullifying it with evidence to the contrary.
A type I error occurs when detecting an effect (adding water to toothpaste protects against cavities) that is not present. The null hypothesis is true (i.e., it is true that adding water to toothpaste has no effect on cavities), but this null hypothesis is rejected based on bad experimental data.
Example 2[edit]
Hypothesis: "Adding fluoride to toothpaste protects against cavities."
Null hypothesis (H0): "Adding fluoride to toothpaste has no effect on cavities."
This null hypothesis is tested against experimental data with a view to nullifying it with evidence to the contrary.
A type II error occurs when failing to detect an effect (adding fluoride to toothpaste protects against cavities) that is present. The null hypothesis is false (i.e., adding fluoride is actually effective against cavities), but the experimental data is such that the null hypothesis cannot be rejected.
Example 3[edit]
Hypothesis: "The evidence produced before the court proves that this man is guilty."
Null hypothesis (H0): "This man is innocent."
A type I error occurs when convicting an innocent person (a miscarriage of justice). A type II error occurs when letting a guilty person go free (an error of impunity).
A positive correct outcome occurs when convicting a guilty person. A negative correct outcome occurs when letting an innocent person go free.
Example 4[edit]
Hypothesis: "A patient's symptoms improve after treatment A more rapidly than after a placebo treatment."
Null hypothesis (H0): "A patient's symptoms after treatment A are indistinguishable from a placebo."
A Type I error would falsely indicate that treatment A is more effective than the placebo, whereas a Type II error would be a failure to demonstrate that treatment A is more effective than placebo even though it actually is more effective.
Etymology[edit]
In 1928, Jerzy Neyman (1894–1981) and Egon Pearson (1895–1980), both eminent statisticians, discussed the problems associated with "deciding whether or not a particular sample may be judged as likely to have been randomly drawn from a certain population"[8]p. 1: and, as Florence Nightingale David remarked, "it is necessary to remember the adjective 'random' [in the term 'random sample'] should apply to the method of drawing the sample and not to the sample itself".[9]
They identified "two sources of error", namely:
(a) the error of rejecting a hypothesis that should have been accepted, and
(b) the error of accepting a hypothesis that should have been rejected.[8]p.31
In 1930, they elaborated on these two sources of error, remarking that:
...in testing hypotheses two considerations must be kept in view, (1) we must be able to reduce the chance of rejecting a true hypothesis to as low a value as desired; (2) the test must be so devised that it will reject the hypothesis tested when it is likely to be false.[10]
In 1933, they observed that these "problems are rarely presented in such a form that we can discriminate with certainty between the true and false hypothesis" (p. 187). They also noted that, in deciding whether to accept or reject a particular hypothesis amongst a "set of alternative hypotheses" (p. 201), H1, H2, . . ., it was easy to make an error:
...[and] these errors will be of two kinds:
(I) we reject H0 [i.e., the hypothesis to be tested] when it is true,
(II) we accept H0 when some alternative hypothesis HA or H1 is true.[11]p.187 (There are various notations for the alternative).
In all of the papers co-written by Neyman and Pearson the expression H0 always signifies "the hypothesis to be tested".
In the same paper[11]p. 190 they call these two sources of error, errors of type I and errors of type II respectively.
Related terms[edit]
See also: Coverage probability
Null hypothesis[edit]
Main article: Null hypothesis
It is standard practice for statisticians to conduct tests in order to determine whether or not a "speculative hypothesis" concerning the observed phenomena of the world (or its inhabitants) can be supported. The results of such testing determine whether a particular set of results agrees reasonably (or does not agree) with the speculated hypothesis.
On the basis that it is always assumed, by statistical convention, that the speculated hypothesis is wrong, and the so-called "null hypothesis" that the observed phenomena simply occur by chance (and that, as a consequence, the speculated agent has no effect) – the test will determine whether this hypothesis is right or wrong. This is why the hypothesis under test is often called the null hypothesis (most likely, coined by Fisher (1935, p. 19)), because it is this hypothesis that is to be either nullified or not nullified by the test. When the null hypothesis is nullified, it is possible to conclude that data support the "alternative hypothesis" (which is the original speculated one).
The consistent application by statisticians of Neyman and Pearson's convention of representing "the hypothesis to be tested" (or "the hypothesis to be nullified") with the expression H0 has led to circumstances where many understand the term "the null hypothesis" as meaning "the nil hypothesis" – a statement that the results in question have arisen through chance. This is not necessarily the case – the key restriction, as per Fisher (1966), is that "the null hypothesis must be exact, that is free from vagueness and ambiguity, because it must supply the basis of the 'problem of distribution,' of which the test of significance is the solution."[12] As a consequence of this, in experimental science the null hypothesis is generally a statement that a particular treatment has no effect; in observational science, it is that there is no difference between the value of a particular measured variable, and that of an experimental prediction.
Statistical significance[edit]
If the probability of obtaining a result as extreme as the one obtained, supposing that the null hypothesis were true, is lower than a pre-specified cut-off probability (for example, 5%), then the result is said to be statistically significant and the null hypothesis is rejected.
British statistician Sir Ronald Aylmer Fisher (1890–1962) stressed that the "null hypothesis":
... is never proved or established, but is possibly disproved, in the course of experimentation. Every experiment may be said to exist only in order to give the facts a chance of disproving the null hypothesis.
—  1935, p.19
Application domains[edit]
Statistical tests always involve a trade-off between:
the acceptable level of false positives (in which a non-match is declared to be a match) and
the acceptable level of false negatives (in which an actual match is not detected).
A threshold value can be varied to make the test more restrictive or more sensitive, with the more restrictive tests increasing the risk of rejecting true positives, and the more sensitive tests increasing the risk of accepting false positives.
Inventory control[edit]
An automated inventory control system that rejects high-quality goods of a consignment commits a type I error, while a system that accepts low-quality goods commits a type II error.
Computers[edit]
The notions of false positives and false negatives have a wide currency in the realm of computers and computer applications, as follows.
Computer security[edit]
Main articles: computer security and computer insecurity
Security vulnerabilities are an important consideration in the task of keeping computer data safe, while maintaining access to that data for appropriate users. Moulton (1983), stresses the importance of:
avoiding the type I errors (or false positives) that classify authorized users as imposters.
avoiding the type II errors (or false negatives) that classify imposters as authorized users.
Spam filtering[edit]
A false positive occurs when spam filtering or spam blocking techniques wrongly classify a legitimate email message as spam and, as a result, interferes with its delivery. While most anti-spam tactics can block or filter a high percentage of unwanted emails, doing so without creating significant false-positive results is a much more demanding task.
A false negative occurs when a spam email is not detected as spam, but is classified as non-spam. A low number of false negatives is an indicator of the efficiency of spam filtering.
Malware[edit]
The term "false positive" is also used when antivirus software wrongly classifies a harmless file as a virus. The incorrect detection may be due to heuristics or to an incorrect virus signature in a database. Similar problems can occur with antitrojan or antispyware software.
Optical character recognition[edit]
Detection algorithms of all kinds often create false positives. Optical character recognition (OCR) software may detect an "a" where there are only some dots that appear to be an "a" to the algorithm being used.
Security screening[edit]
Main articles: explosive detection and metal detector
False positives are routinely found every day in airport security screening, which are ultimately visual inspection systems. The installed security alarms are intended to prevent weapons being brought onto aircraft; yet they are often set to such high sensitivity that they alarm many times a day for minor items, such as keys, belt buckles, loose change, mobile phones, and tacks in shoes.
The ratio of false positives (identifying an innocent traveller as a terrorist) to true positives (detecting a would-be terrorist) is, therefore, very high; and because almost every alarm is a false positive, the positive predictive value of these screening tests is very low.
The relative cost of false results determines the likelihood that test creators allow these events to occur. As the cost of a false negative in this scenario is extremely high (not detecting a bomb being brought onto a plane could result in hundreds of deaths) whilst the cost of a false positive is relatively low (a reasonably simple further inspection) the most appropriate test is one with a low statistical specificity but high statistical sensitivity (one that allows a high rate of false positives in return for minimal false negatives).
Biometrics[edit]
Biometric matching, such as for fingerprint recognition, facial recognition or iris recognition, is susceptible to type I and type II errors. The null hypothesis is that the input does identify someone in the searched list of people, so:
the probability of type I errors is called the "false reject rate" (FRR) or false non-match rate (FNMR),
while the probability of type II errors is called the "false accept rate" (FAR) or false match rate (FMR).[13]
If the system is designed to rarely match suspects[disambiguation needed] then the probability of type II errors can be called the "false alarm rate". On the other hand, if the system is used for validation (and acceptance is the norm) then the FAR is a measure of system security, while the FRR measures user inconvenience level.
Medicine[edit]
Further information: False positives and false negatives
Medical screening[edit]
In the practice of medicine, there is a significant difference between the applications of screening and testing.
Screening involves relatively cheap tests that are given to large populations, none of whom manifest any clinical indication of disease (e.g., Pap smears).
Testing involves far more expensive, often invasive, procedures that are given only to those who manifest some clinical indication of disease, and are most often applied to confirm a suspected diagnosis.
For example, most states in the USA require newborns to be screened for phenylketonuria and hypothyroidism, among other congenital disorders. Although they display a high rate of false positives, the screening tests are considered valuable because they greatly increase the likelihood of detecting these disorders at a far earlier stage.[Note 1]
The simple blood tests used to screen possible blood donors for HIV and hepatitis have a significant rate of false positives; however, physicians use much more expensive and far more precise tests to determine whether a person is actually infected with either of these viruses.
Perhaps the most widely discussed false positives in medical screening come from the breast cancer screening procedure mammography. The US rate of false positive mammograms is up to 15%, the highest in world. One consequence of the high false positive rate in the US is that, in any 10-year period, half of the American women screened receive a false positive mammogram. False positive mammograms are costly, with over $100 million spent annually in the U.S. on follow-up testing and treatment. They also cause women unneeded anxiety. As a result of the high false positive rate in the US, as many as 90–95% of women who get a positive mammogram do not have the condition. The lowest rate in the world is in the Netherlands, 1%. The lowest rates are generally in Northern Europe where mammography films are read twice and a high threshold for additional testing is set (the high threshold decreases the power of the test).
The ideal population screening test would be cheap, easy to administer, and produce zero false-negatives, if possible. Such tests usually produce more false-positives, which can subsequently be sorted out by more sophisticated (and expensive) testing.
Medical testing[edit]
False negatives and false positives are significant issues in medical testing. False negatives may provide a falsely reassuring message to patients and physicians that disease is absent, when it is actually present. This sometimes leads to inappropriate or inadequate treatment of both the patient and their disease. A common example is relying on cardiac stress tests to detect coronary atherosclerosis, even though cardiac stress tests are known to only detect limitations of coronary artery blood flow due to advanced stenosis.
False negatives produce serious and counter-intuitive problems, especially when the condition being searched for is common. If a test with a false negative rate of only 10%, is used to test a population with a true occurrence rate of 70%, many of the negatives detected by the test will be false.
False positives can also produce serious and counter-intuitive problems when the condition being searched for is rare, as in screening. If a test has a false positive rate of one in ten thousand, but only one in a million samples (or people) is a true positive, most of the positives detected by that test will be false. The probability that an observed positive result is a false positive may be calculated using Bayes' theorem.
Paranormal investigation[edit]
The notion of a false positive is common in cases of paranormal or ghost phenomena seen in images and such, when there is another plausible explanation. When observing a photograph, recording, or some other evidence that appears to have a paranormal origin, a false positive is a disproven piece of media "evidence" (image, movie, audio recording, etc.) that actually has a natural explanation.[Note 2]
See also[edit]
Statistics portal
Binary classification
Detection theory
Egon Pearson
Ethics in mathematics
False positive paradox
Family-wise error rate
Information retrieval performance measures
Neyman–Pearson lemma
Null hypothesis
Probability of a hypothesis for Bayesian inference
Precision and recall
Prosecutor's fallacy
Prozone phenomenon
Receiver operating characteristic
Sensitivity and specificity
Statisticians' and engineers' cross-reference of statistical terms
Testing hypotheses suggested by the data
Type III error
Notes[edit]
Jump up ^ In relation to this newborn screening, recent studies have shown that there are more than 12 times more false positives than correct screens (Gambrill, 2006. [1])[dead link]
Jump up ^ Several sites provide examples of false positives, including The Atlantic Paranormal Society (TAPS) Archived 28 March 2005 at the Wayback Machine. and Moorestown Ghost Research.
References[edit]
Jump up ^ "Type I Error and Type II Error - Experimental Errors". explorable.com. Retrieved 2016-05-30.
^ Jump up to: a b Sheskin, David (2004). Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press. p. 54. ISBN 1584884401.
Jump up ^ Peck, Roxy and Jay L. Devore (2011). Statistics: The Exploration and Analysis of Data. Cengage Learning. pp. 464–465. ISBN 0840058012.
Jump up ^ Cisco Secure IPS – Excluding False Positive Alarms http://www.cisco.com/en/US/products/hw/vpndevc/ps4077/products_tech_note09186a008009404e.shtml
^ Jump up to: a b Lindenmayer, David; Burgman, Mark A. (2005). "Monitoring, assessment and indicators". Practical Conservation Biology (PAP/CDR ed.). Collingwood, Victoria, Australia: CSIRO Publishing. pp. 401–424. ISBN 0-643-09089-4.
Jump up ^ Schlotzhauer, Sandra (2007). Elementary Statistics Using JMP (SAS Press) (1 ed.). Cary, NC: SAS Institute. pp. 166–423. ISBN 1-599-94375-1.
Jump up ^ Shermer, Michael (2002). The Skeptic Encyclopedia of Pseudoscience 2 volume set. ABC-CLIO. p. 455. ISBN 1-57607-653-9. Retrieved 10 January 2011.
^ Jump up to: a b Neyman, J.; Pearson, E.S. (1967) [1928]. "On the Use and Interpretation of Certain Test Criteria for Purposes of Statistical Inference, Part I". Joint Statistical Papers. Cambridge University Press. pp. 1–66.
Jump up ^ David, F.N. (1949). Probability Theory for Statistical Methods. Cambridge University Press. p. 28.
Jump up ^ Pearson, E.S.; Neyman, J. (1967) [1930]. "On the Problem of Two Samples". Joint Statistical Papers. Cambridge University Press. p. 100.
^ Jump up to: a b Neyman, J.; Pearson, E.S. (1967) [1933]. "The testing of statistical hypotheses in relation to probabilities a priori". Joint Statistical Papers. Cambridge University Press. pp. 186–202.
Jump up ^ Fisher, R.A. (1966). The design of experiments. 8th edition. Hafner:Edinburgh.
Jump up ^ Williams, G.O. (1996). "Iris Recognition Technology" (PDF). debut.cis.nctu.edu.tw. p. 56. Retrieved 2010-05-23. crossover error rate (that point where the probabilities of False Reject (Type I error) and False Accept (Type II error) are approximately equal) is .00076%
Betz, M.A. & Gabriel, K.R., "Type IV Errors and Analysis of Simple Effects", Journal of Educational Statistics, Vol.3, No.2, (Summer 1978), pp. 121–144.
David, F.N., "A Power Function for Tests of Randomness in a Sequence of Alternatives", Biometrika, Vol.34, Nos.3/4, (December 1947), pp. 335–339.
Fisher, R.A., The Design of Experiments, Oliver & Boyd (Edinburgh), 1935.
Gambrill, W., "False Positives on Newborns' Disease Tests Worry Parents", Health Day, (5 June 2006). 34471.html[dead link]
Kaiser, H.F., "Directional Statistical Decisions", Psychological Review, Vol.67, No.3, (May 1960), pp. 160–167.
Kimball, A.W., "Errors of the Third Kind in Statistical Consulting", Journal of the American Statistical Association, Vol.52, No.278, (June 1957), pp. 133–142.
Lubin, A., "The Interpretation of Significant Interaction", Educational and Psychological Measurement, Vol.21, No.4, (Winter 1961), pp. 807–817.
Marascuilo, L.A. & Levin, J.R., "Appropriate Post Hoc Comparisons for Interaction and nested Hypotheses in Analysis of Variance Designs: The Elimination of Type-IV Errors", American Educational Research Journal, Vol.7., No.3, (May 1970), pp. 397–421.
Mitroff, I.I. & Featheringham, T.R., "On Systemic Problem Solving and the Error of the Third Kind", Behavioral Science, Vol.19, No.6, (November 1974), pp. 383–393.
Mosteller, F., "A k-Sample Slippage Test for an Extreme Population", The Annals of Mathematical Statistics, Vol.19, No.1, (March 1948), pp. 58–65.
Moulton, R.T., “Network Security”, Datamation, Vol.29, No.7, (July 1983), pp. 121–127.
Raiffa, H., Decision Analysis: Introductory Lectures on Choices Under Uncertainty, Addison–Wesley, (Reading), 1968.
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Bias and Confounding – presentation by Nigel Paneth, Graduate School of Public Health, University of Pittsburgh
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A Regional Health Information Organization (RHIO, pronounced rio), also called a Health Information Exchange Organization, is a multistakeholder organization created to facilitate a health information exchange (HIE) – the transfer of healthcare information electronically across organizations – among stakeholders of that region's healthcare system. The ultimate objective is to improve the safety, quality, and efficiency of healthcare as well as access to healthcare through the efficient application of health information technology. RHIOs are also intended to support secondary use of clinical data for research as well as institution/provider quality assessment and improvement.[1] RHIO stakeholders include smaller clinics, hospitals, medical societies, major employers and payers.
RHIOs are intended to be key to the proposed US Nationwide Health Information Network (NHIN).[2][not in citation given]
Contents
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1 Background
2 Architecture types
3 Current status
4 Examples
4.1 INPC and IHIE
4.2 CalRHIO
4.3 Other examples
5 See also
6 References
7 External links
7.1 RHIO sites
Background[edit]
The US system of healthcare is highly complex and fragmented, with use of multiple information technology systems and vendors incorporating different standards. In contrast with other developed nations, healthcare accounts for a disproportionate percentage of the US GDP, and there is a concern about the economic viability of Medicare.[3] Despite this significant expenditure, there is evidence of inefficiency, waste and medical errors, with a 2001 estimate by the Institute of Medicine of between 44,000-98,000 annual deaths due to medical errors.[4] While numerous possible causes for this situation exist, much waste (e.g., duplication of laboratory tests) and medical errors (e.g., adverse drug reactions) are traceable to inability of healthcare providers (who are not the patients' primary providers) to access patients' existing medical information in a timely manner when needed: better and more ubiquitous health information technology might be part of the overall solution.
In 2004 President W. George Bush issued Executive Order 13335 for the development and nationwide implementation of an interoperable health information technology infrastructure to improve the quality and efficiency of health care with the goal that most Americans will have an EHR by 2014.[5] In July 2004 the United States Department of Health and Human Services released their vision of how America’s healthcare system could be rebuilt during the next decade.[6] An aspect of the federal effort is the creation of the Office of the National Coordinator for Health Information Technology.[5] One of the roles of ONC is to facilitate RHIO development.[7]
The planning stage of RHIO formation involves
identification of a shared vision/motivation
creating a governance structure
identifying a technology and network infrastructure for data integration
defining standards for data sharing, protection of data, and business practices to ensure patient protection during data exchange
defining educational and business strategies to ensure sustainability of the effort.
Finally, RHIOs must implement the proposed strategies.[citation needed]
Architecture types[edit]
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The two broad types of architecture, centralized and federated, correspond to the data warehouse and federated database system models of data integration. In the centralized configuration (e.g., The Santa Cruz Community and the Michigan UP Network), all providers send their data to the RHIO's central repository on a periodic basis (daily). In the federated model, the RHIO acts as a record locator service, the data stays at its original location, and the RHIO only has a "pointer" to that information.
The pros and cons of each architecture follow from the approaches.
Centralized
For the Centralized design, once data is centralized and restructured into a uniform data model, it is easier to query and analyze. However, because movement and restructuring is generally a complex batch process involving the well-known steps of "extract, transform, load", the centralized data may be somewhat out of date if the (logistically challenging) target of daily updates is not achieved. Also, there may be concerns among the individual RHIO participants who originate the raw data that they are giving up "control" and "ownership" once the data is copied to a central site. Also, creation of the central repository requires close collaboration to determine exactly what data will be centralized and how it will be structured.
Federated
In federated systems the RHIO software only stores information about the locations where a patient's data is available. These are often more politically feasible than centralized systems. However, designing a protocol by which the RHIO can query (heterogeneously structured) individual provider data stores is technically challenging. The software at the individual sites must ensure authenticate electronic requests to ensure that they are legitimate and authorized, and no standards that can be used for this purpose currently exist. A federated setup requires greater network bandwidth than the centralized approach, because a request by a user of the central RHIO software can be farmed out to multiple provider systems.
Centralized and federated approaches are not mutually exclusive, and hybrid setups can be employed. In one proposed hybrid model, the health record trust or health record data bank, all data for each patient would stay in a single repository as in the centralized model, but patients could choose which data bank to use for their records.[8]
Current status[edit]
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As of 2009[update], 193 initiatives (including RHIOs) in the United States were working on health information exchange, and 57 of those were actively exchanging data used by healthcare stakeholders.[9] RHIO initiatives exist at two levels, larger (statewide) and smaller (local and rural).
Compared to local RHIOs, state-level initiatives, because of the larger number of stakeholders and patient base, and consequent financial strength, have a greater likelihood of being able to utilize national-level expertise both in health IT and policy-making. Most operational RHIO efforts tend to be larger efforts, though the risk of political battles and consequent failure is also amplified if several large stakeholders choose not to cooperate with each other.
Compared to state-level RHIOs, stakeholders tend to be more heterogeneous with respect to line of business, use of technology and size. Rural RHIOs have a subscriber base of less than 100,000. The presence of numerous, smaller organizations with relatively limited IT budgets translates to numerous challenges due to the dis-economies of small scale. There is limited access to skilled technology professions, and health IT vendors pay less attention to small customers. Many stakeholders continue to deploy legacy software from long-defunct vendors and some may not deploy IT at all. In addition, stakeholders may be less IT-savvy, and even availability of high-speed Internet connectivity may not be assured. A significant sustainability challenge for smaller RHIOs involves funding. Some support models (e.g., used by Northwest, Louisville KY) are based on shared, subscription-based use of commercial health information software, and the shared use of scarce resources such as information technology professionals, who are unaffordable for smaller organizations acting individually.
Irrespective of size, certain challenges remain, such as interoperability standards, as well as consistent standards with respect to privacy, security and appropriate use of the data. Currently,[when?] privacy/security issues vary across states, and federal efforts to ensure uniformity are desirable.
Examples[edit]
INPC and IHIE[edit]
The Indiana Network for Patient Care (INPC) — originally the Indianapolis Network for Patient Care and Research (INPRC)[10] — operational since 1994,[11] connects all five major hospital systems in greater Indianapolis. All INPC participants now deliver registration records, all laboratory tests, text reports, medication history and all UB92 records (diagnosis, length of stay, and procedure codes) for hospital admissions and emergency room visits to separate electronic medical record vaults in a central INPC server using a federated data store model.[11][12] The message exchange standard that is used is HL7. The computer system standardizes all clinical data as it arrives at the INPC vault, laboratory, radiology, and other study test results are mapped to a set of common test codes LOINC with standard units of measure, and patients with multiple medical record numbers are linked.[11]
Implementation has been simplified because each participating institution uses the same data model and term dictionary which contain the codes, names (and other attributes) for tests, drugs, coded answers, etc. The data for any participating patient seen in any of the region's 11 emergency rooms can be presented as one virtual medical record.[13] Stakeholders/ participants in this RHIO include: major and regional hospital systems, regional and national reference laboratories, state, federal, and private payors, pharmacy benefit management claims repository services, major physician practice organizations, individual providers, state government (Dept. of Health), major academic research institutions, and others.
The statewide Indiana Health Information Exchange (IHIE), which uses the expertise of the same medical informatics/IT team as the INPC, combines clinical data in combination with administrative (claims) data to support electronic delivery of test reports to physician offices using faxes, printers, and e-mail, as well as Clinical Quality Services, which provide a "report card" (and incentives) on established quality measures to physicians, practice groups, employers, and payers. The IHIE is a rare example of a self-sustaining health information exchange (not reliant on government grants). Stakeholder satisfaction with this RHIO is apparently high.[14]
CalRHIO[edit]
CalRHIO is an example of an RHIO failure due to political/turf battles; it shutdown January 8, 2010.[citation needed] Following heavy competition with California e-Health Collaborative (CAeHC) for designation as California's statewide HIE entity, both organizations were turned down by the state government in December 2009;[15] large stakeholders such as Medicity Inc. and Kaiser Permanente subsequently followed suit and withdrew financial support. Reasons for CalRHIO's failure have been attributed[by whom?] to a limited business model dependent on state-entity designation.[citation needed]
Other examples[edit]
This section does not cite any sources. (September 2016) (Learn how and when to remove this template message)
In some states (e.g., Kansas), an alternative to a stakeholder-consortium-based state-level RHIO is being considered, namely direct involvement of the state governments in creating the RHIO infrastructure. This approach builds on the strengths of state governments as major employers and purchasers of healthcare services (e.g., University of Alabama at Birmingham, is Alabama's largest employer). Similarly, TennCare in Tennessee has roots in addressing the state’s un- and under-insured population and is consequently more state driven. Utah’s efforts are based on the state’s prior efforts to process healthcare claims.
Recent federal efforts (e.g., at the Veterans Administration) are aimed at creating a federal employee based electronic health record system, which might be considered a national RHIO.
See also[edit]
Chesapeake Regional Information System for our Patients (CRISP) — RHIO serving Maryland the District of Columbia
HealthBridge — RHIO for the Greater Cincinnati area (Ohio, Kentucky, & Indiana)
Informatics Corporation of America — a RHIO vendor
PeaceHealth — Medical Group in Alaska, Oregon, & Washington
References[edit]
Jump up ^ "What is Health Information Exchange?" (PDF). American College of Rheumatology. 17 August 2010. p. 1. Archived from the original (PDF) on 19 April 2012.
Jump up ^ "Transforming Health Care: The President's Health Information Technology Plan, Promoting Innovation and Competitiveness: A new generation of innovation". The White House: President George W. Bush. National Archives and Records Administration. 24 January 2004.
Jump up ^ "U.S. health costs on unyielding rise". CNN. Cable News Network LP, LLLP. Associated Press. 22 February 2006. Archived from the original on 14 March 2006.
Jump up ^ Committee on Quality of Health Care in America; Institute of Medicine (2000). Kohn, Linda T.; Corrigan, Janet M.; Donaldson, Molla S., eds. To Err Is Human: Building a Safer Health System. Washington, DC: National Academy Press. doi:10.17226/9728. ISBN 978-0-309-26174-6.
^ Jump up to: a b "Office of the National Coordinator for Health Information Technology (ONC)". United States Department of Health & Human Services. 9 November 2004. Archived from the original on 21 December 2008.
Jump up ^ "Health IT Strategic Framework". United States Department of Health and Human Services. 13 October 2004. Archived from the original on 14 October 2004.
Jump up ^ "Thompson Launches "Decade of Health Information Technology"" (Press release). United States Department of Health and Human Services. 21 June 2004. Archived from the original on 15 October 2004.
Jump up ^ Castro, Daniel (October 2007), Improving Health Care: How a Dose of IT May Be Just What the Doctor Ordered (PDF), The Information Technology and Innovation Foundation, pp. 10, 12.
Jump up ^ Migrating Toward Meaningful Use: The State of Health Information Exchange. A Report Based on the Results of the eHealth Initiative's 2009 Sixth Annual Survey of Health Information Exchange (PDF) (Report). Washington, DC: eHealth Initiative. August 2009. pp. 4–5.
Jump up ^ Overhage, JM; Tierney, WM; McDonald, CJ (January 1995). "Design and implementation of the Indianapolis Network for Patient Care and Research". Bulletin of the Medical Library Association. 83 (1): 48–56. PMC 225997  . PMID 7703939.
^ Jump up to: a b c "Indiana Network For Patient Care". Regenstrief Institute. Archived from the original on 31 July 2012.
Jump up ^ McDonald, Clement J.; Overhage, J. Marc; Barnes, Michael; Schadow, Gunther; Blevins, Lonnie; Dexter, Paul R.; Mamlin, Burke; INPC Management Committee (September 2005). "The Indiana Network For Patient Care: A Working Local Health Information Infrastructure". Health Affairs. 24 (5): 1214–1220. doi:10.1377/hlthaff.24.5.1214. PMID 16162565.
Jump up ^ Finnell, John T.; Overhage, J. Marc; Dexter, Paul R.; Perkins, Susan M.; Lane, Kathleen A.; McDonald, Clement J. (2003). "Community Clinical Data Exchange for Emergency Medicine Patients". AMIA Annual Symposium Proceedings. 2003: 235–238. PMC 1480174  . PMID 14728169.
Jump up ^ Terry, Ken (7 October 2005). "Why these doctors love their RHIO". Medical Economics. Advanstar Communications. Archived from the original on 25 October 2006.
Jump up ^ Rauber, Chris (January 10, 2010). "CalRHIO closes, but board to help state on IT". San Francisco Business Times. American City Business Journals. (Registration required (help)).
External links[edit]
Office of the National Coordinator for Health Information Technology
AHRQ National Resource Center for Health Information Technology
RHIO sites[edit]
Hixny — Albany-region and Northern New York
Rochester RHIO — Rochester, New York
Indiana Health Information Exchange
Medical Information Network – North Sound (MIN-NS) — Mount Vernon, Washington
BronxRHIO — Brox, New York
CORHIO — Colorado
Keystone Health Information Exchange (KeyHIE) — Pennsylvania
Chesapeake Regional Information System for our Patients (CRISP) — Maryland the District of Columbia
Midsouth e-Health Alliance — Memphis-area, Tennessee
HealthLink New York — Hudson Valley, Catskills, and Southern Tier, New York
HEALTHeLINK: Western New York Clinical Information Exchange — Buffalo, New York
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