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
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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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United States men's national ice hockey team
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United States
Nickname(s)
Team U.S.A., Ice Yanks
Association
USA Hockey
General Manager
Bill Zito
Head coach
Jeff Blashill
Assistants
Dan Bylsma
Don Granato
Seth Appert
Captain
Patrick Kane
Most games
Mark Johnson (151)
Most points
Mark Johnson (146)
Team colors
IIHF code
USA
Ranking
Current IIHF
6 1
Highest IIHF
4 (2016)
Lowest IIHF
7 (first in 2003)
First international
United States 29–0 Switzerland
(Antwerp, Belgium; April 24, 1920)
Biggest win
United States 31–1 Italy
(St. Moritz, Switzerland; February 1, 1948)
Biggest defeat
Sweden 17–2 United States
(Stockholm, Sweden; March 12, 1963)
Soviet Union 17–2 United States
(Stockholm, Sweden; March 15, 1969)
IIHF World Championships
Appearances
69 (first in 1920)
Best result
Gold: (1933, 1960)
World Cup / Canada Cup
Appearances
8 (first in 1976)
Best result
Winner: (1996)
Olympics
Appearances
22 (first in 1920)
Medals
Gold (1960, 1980)
Silver (1920, 1924, 1932, 1952, 1956, 1972, 2002, 2010)
Bronze: (1936)
Medal record
Olympic Games
1960 Squaw Valley Team
1980 Lake Placid Team
1920 Antwerp Team
1924 Chamonix Team
1932 Lake Placid Team
1952 Oslo Team
1956 Cortina d'Ampezzo Team
1972 Sapporo Team
2002 Salt Lake City Team
2010 Vancouver Team
1936 Garmisch-Partenkirchen Team
Canada Cup/World Cup of Hockey
1996 World Cup of Hockey
1991 Canada Cup
World Championship
1933 Czechoslovakia
1931 Poland
1934 Italy
1939 Switzerland
1950 Great Britain
1949 Sweden
1962 United States
1996 Austria
2004 Czech Republic
2013 Sweden/Finland
2015 Czech Republic
The United States men's national ice hockey team is based in Colorado Springs, Colorado, with its U18 and U17 development program in Plymouth, Michigan. The team is controlled by USA Hockey, the governing body for organized ice hockey in the United States. The US team is ranked 6th in the IIHF World Rankings.[1] The current head coach is Tony Granato.[2]
The United States won gold medals at the 1960 and 1980 Winter Olympics and more recently, silver medals at the 2002 and 2010 Winter Olympics. The United States won the 1996 World Cup of Hockey. The team's most recent medal at the World Championships came with a bronze in 2015. They won the tournament in 1933 and 1960. Unlike other nations, the United States doesn't typically use its best NHL players in the World Championships even when they're available. Instead, USA Hockey uses this tournament as a platform for young NHLers and college players.
United States is a member of the so-called "Big Six", the unofficial group of the six strongest men's ice hockey nations, along with Canada, the Czech Republic, Finland, Russia, and Sweden.[3]
As of 2014, the US has a registered ice hockey population of 611,926 with USA Hockey.[4] USA Hockey is the largest governing body for ice hockey in the United States and is considered the best representation of the number of players playing ice hockey in the US.[5]
Contents
[hide]
1 History
2 Tournament record
2.1 Olympic Games
2.2 Canada Cup
2.3 World Cup
2.4 World Championship
2.5 Others
3 Team
3.1 Current roster
3.2 IIHF World Championship directorate awards
4 See also
5 References
6 External links
History[edit]
The American ice hockey team's greatest success was the "Miracle on Ice" at the 1980 Winter Olympics in Lake Placid, New York when American college players defeated the heavily favored professionals from the Soviet Union on the way to a gold medal. Though hockey is not a major sport in most areas of the United States, the "Miracle" is often listed as one of the all-time greatest American sporting achievements. The United States also won the gold medal in the 1960 Games at Squaw Valley, California, defeating the Soviet Union, Canada, Czechoslovakia, and Sweden along the way. However, since this victory is not as well known as the 1980 win, it has come to be known as the "Forgotten Miracle".[6][7]
U.S. hockey experienced a spike in talent in the 1980s and 1990s, with future National Hockey League (NHL) stars including Tony Amonte, Chris Chelios, Derian Hatcher, Brett Hull, Pat LaFontaine, John LeClair, Brian Leetch, Mike Modano, Mike Richter, Jeremy Roenick, Kevin Stevens, Keith Tkachuk, and Doug Weight. Although the United States finished no higher than fourth in any World or Olympic event from 1981 through 1994 (the US never used its best in these tournaments), the Americans did reach the final of the 1991 Canada Cup and did win the 1996 World Cup with a squad of NHL players. Six years later, after the International Olympic Committee (IOC) and NHL arranged to accommodate an Olympic break in the NHL schedule, the United States earned a silver medal at the 2002 Winter Olympics with a roster that included NHL stars Adam Deadmarsh, Chris Drury, Brian Rafalski, and Brian Rolston. But by 2006, many of these NHL All-Stars had retired or had declined with age. Though the 2006 Olympic team finished a disappointing 8th, it was more of a transitional team, featuring young NHL players like Rick DiPietro, John-Michael Liles, and Jordan Leopold.
The 2010 U.S. Olympic team was composed of much younger and faster players than teams of previous years, including David Backes, Dustin Brown, Jack Johnson, Patrick Kane, Phil Kessel, Zach Parise, Joe Pavelski, Bobby Ryan, Paul Stastny, and Ryan Suter. The team also had a solid group of veterans that included top NHL goalie Ryan Miller top defenseman Brian Rafalski and U.S. Olympic Team Captain Jamie Langenbrunner. The U.S. team upset team Canada 5–3 in the round-robin phase of the tournament and went into the single elimination phase of the tournament as the number-one seeded team. After beating Finland 6–1 the United States advanced to the gold medal game, where they lost in overtime 3–2 to Canada to claim the silver medal. The gold medal game between Canada and the United States was watched by an estimated 27.6 million U.S. households. This was the most watched hockey game in America since the 1980 "Miracle on Ice" game, including any Stanley Cup Final or NHL Winter Classic broadcast.[8]
The NHL pulled out of the Olympics for the 2018 competition in a dispute over insurance and the IOC's ambush marketing restrictions, prohibiting the national teams from inviting any player it held under contract. On January 1, 2018, the US roster was announced. Five players both from the Swiss National League and the KHL, four players from the NCAA, three players from the Swedish League and the AHL and two players from the German League made the team. Brian Gionta, the captain, is the lone unsigned free agent in the 25-men roster (he was signed to a practice squad contract with an AHL team at the time).[9] The team eventually lost in the quarterfinals; the Olympic Athletes from Russia (OAR) team, consisting primarily of SKA and CSKA players of a Russia-based KHL and featuring ex-NHL all-stars Pavel Datsyuk, Ilya Kovalchuk and Vyacheslav Voynov (all SKA), won the gold medal, defeating the German national team with a score of 4:3 OT in the final. Canada won a bronze medal over the Czech Republic.
Tournament record[edit]
Olympic Games[edit]
Year
Result
1920
1924
1928 did not participate
1932
1936
1948 disqualified
1952
1956
1960
1964 5th place
1968 6th place
1972
1976 5th place
1980
1984 7th place
1988 7th place
1992 4th place
1994 8th place
1998 6th place
2002
2006 8th place
2010
2014 4th place
2018 7th place
Totals
Games
Gold
Silver
Bronze
Total
21 2 8 1 11
Canada Cup[edit]
1976 — 5th
1981 — 4th
1984 — 4th
1987 — 5th
1991 — 2nd
World Cup[edit]
1996 — 1st
2004 — 4th
2016 (24 and older) — 7th
World Championship[edit]
See: Ice Hockey World Championships and List of IIHF World Championship medalists
Note: Between 1920 and 1968, the Olympic hockey tournament was also considered the World Championship for that year.[10]
1920 – Won medal
1924 – Won medal
1931 – Won medal
1932 – Won medal
1933 – Won medal
1934 – Won medal
1936 – Won medal
1938 – Finished in 7th place
1939 – Won medal
1940–46 – Not held[11]
1947 – Finished in 5th place
1948 – Finished in 4th place
1949 – Won medal
1950 – Won medal
1951 – Finished in 6th place
1952 – Won medal
1955 – Finished in 4th place
1956 – Won medal
1958 – Finished in 5th place
1959 – Finished in 4th place
1960 – Won medal
1961 – Finished in 6th place
1962 – Won medal
1963 – Finished in 8th place
1964 – Finished in 5th place
1965 – Finished in 6th place
1966 – Finished in 6th place
1967 – Finished in 5th place
1968 – Finished in 6th place
1969 – Finished in 6th place
1970 – Finished in 7th place (Won "Pool B")
1971 – Finished in 6th place
1972 – Finished in 8th place (2nd in "Pool B")[12]
1973 – Finished in 8th place (2nd in "Pool B")
1974 – Finished in 7th place (Won "Pool B")
1975 – Finished in 6th place
1976 – Finished in 4th place
1977 – Finished in 6th place
1978 – Finished in 6th place
1979 – Finished in 7th place
1980 – Not held[13]
1981 – Finished in 5th place
1982 – Finished in 8th place
1983 – Finished in 9th place (Won "Pool B")
1984 – Not held[13]
1985 – Finished in 4th place
1986 – Finished in 6th place
1987 – Finished in 7th place
1988 – Not held[13]
1989 – Finished in 6th place
1990 – Finished in 5th place
1991 – Finished in 4th place
1992 – Finished in 7th place
1993 – Finished in 6th place
1994 – Finished in 4th place
1995 – Finished in 6th place
1996 – Won medal
1997 – Finished in 6th place
1998 – Finished in 12th place
1999 – Finished in 6th place
2000 – Finished in 5th place
2001 – Finished in 4th place
2002 – Finished in 7th place
2003 – Finished in 13th place
2004 – Won medal
2005 – Finished in 6th place
2006 – Finished in 7th place
2007 – Finished in 5th place
2008 – Finished in 6th place
2009 – Finished in 4th place
2010 – Finished in 13th place
2011 – Finished in 8th place
2012 – Finished in 7th place
2013 – Won medal
2014 – Finished in 6th place
2015 – Won medal
2016 – Finished in 4th place
2017 – Finished in 5th place
Others[edit]
1988 Spengler Cup – Winners
2002 Deutschland Cup – Finished in 2nd place[14]
2003 Deutschland Cup – Winners
2004 Deutschland Cup – Winners[15]
2005 Deutschland Cup – Finished in 3rd place[16]
2005 TUI Nations Cup – Finished in 2nd place
2007 Deutschland Cup – Finished in 2nd place[17]
Team[edit]
Current roster[edit]
The following is the American roster for the men's ice hockey tournament at the 2018 Winter Olympics.[18][19]
view
Head coach: Tony Granato Assistant coaches: Keith Allain, Chris Chelios, Scott Young
No.
Pos.
Name
Height
Weight
Birthdate
Birthplace
2017–18 team
4 D Billins, ChadChad Billins 5 ft 8 in (173 cm) 7001789250723800000♠174 lb (79 kg) 000000001989-05-26-0000May 26, 1989 Marysville, Michigan Linköpings HC (SHL)
5 D Welch, NoahNoah Welch – A 6 ft 4 in (193 cm) 7001997903214000000♠220 lb (100 kg) 000000001982-08-26-0000August 26, 1982 Brighton, Massachusetts Växjö Lakers (SHL)
7 F McCarthy, JohnJohn McCarthy 6 ft 1 in (185 cm) 7001879969197800000♠194 lb (88 kg) 000000001986-08-09-0000August 9, 1986 Boston, Massachusetts San Jose Barracuda (AHL)
9 F O'Neill, BrianBrian O'Neill 5 ft 9 in (175 cm) 7001780178876400000♠172 lb (78 kg) 000000001988-06-01-0000June 1, 1988 Yardley, Pennsylvania Jokerit (KHL)
11 F Roe, GarrettGarrett Roe 5 ft 8 in (173 cm) 7001821002189700000♠181 lb (82 kg) 000000001988-02-22-0000February 22, 1988 Vienna, Virginia EV Zug (NL)
12 F Gionta, BrianBrian Gionta – C 5 ft 7 in (170 cm) 7001811930342300000♠179 lb (81 kg) 000000001979-01-18-0000January 18, 1979 Rochester, New York Free agent
13 D Gunderson, RyanRyan Gunderson 5 ft 10 in (178 cm) 7001789250723800000♠174 lb (79 kg) 000000001985-08-16-0000August 16, 1985 Bensalem, Pennsylvania Brynäs IF (SHL)
14 F Little, BrocBroc Little 5 ft 9 in (175 cm) 7001771107029000000♠170 lb (77 kg) 000000001988-03-24-0000March 24, 1988 Phoenix, Arizona HC Davos (NL)
15 F Butler, BobbyBobby Butler 6 ft 0 in (183 cm) 7001861825503000000♠190 lb (86 kg) 000000001987-04-26-0000April 26, 1987 Marlborough, Massachusetts Milwaukee Admirals (AHL)
16 F Donato, RyanRyan Donato 6 ft 0 in (183 cm) 7001870897350400000♠192 lb (87 kg) 000000001996-04-09-0000April 9, 1996 Scituate, Massachusetts Harvard University (ECAC)
17 F Bourque, ChrisChris Bourque 5 ft 8 in (173 cm) 7001789250723800000♠174 lb (79 kg) 000000001986-01-29-0000January 29, 1986 Boston, Massachusetts Hershey Bears (AHL)
18 F Greenway, JordanJordan Greenway 6 ft 6 in (198 cm) 7002102965467990000♠227 lb (103 kg) 000000001997-02-16-0000February 16, 1997 Canton, New York Boston University (HE)
19 F Slater, JimJim Slater 6 ft 0 in (183 cm) 7001861825503000000♠190 lb (86 kg) 000000001982-12-09-0000December 9, 1982 Lapeer, Michigan HC Fribourg-Gottéron (NL)
20 D Borgen, WillWill Borgen 6 ft 2 in (188 cm) 7001848217731900000♠187 lb (85 kg) 000000001996-12-19-0000December 19, 1996 Moorhead, Minnesota St. Cloud State University (NCHC)
21 D Wisniewski, JamesJames Wisniewski 5 ft 11 in (180 cm) 7001920792511100000♠203 lb (92 kg) 000000001984-02-21-0000February 21, 1984 Canton, Michigan Kassel Huskies (DEL2)
22 D Sanguinetti, BobbyBobby Sanguinetti 6 ft 3 in (191 cm) 7001861825503000000♠190 lb (86 kg) 000000001988-02-29-0000February 29, 1988 Trenton, New Jersey HC Lugano (NL)
23 F Terry, TroyTroy Terry 6 ft 1 in (185 cm) 7001789250723800000♠174 lb (79 kg) 000000001997-09-10-0000September 10, 1997 Highlands Ranch, Colorado University of Denver (NCHC)
24 D Blum, JonathonJonathon Blum 6 ft 1 in (185 cm) 7001848217731900000♠187 lb (85 kg) 000000001989-01-30-0000January 30, 1989 Long Beach, California HC Sochi (KHL)
26 F Arcobello, MarkMark Arcobello 5 ft 8 in (173 cm) 7001789250723800000♠174 lb (79 kg) 000000001988-08-12-0000August 12, 1988 Milford, Connecticut SC Bern (NL)
30 G Zapolski, RyanRyan Zapolski 6 ft 0 in (183 cm) 7001920792511100000♠203 lb (92 kg) 000000001986-11-11-0000November 11, 1986 Erie, Pennsylvania Jokerit (KHL)
31 G Maxwell, BrandonBrandon Maxwell 6 ft 1 in (185 cm) 7001889041045200000♠196 lb (89 kg) 000000001991-03-22-0000March 22, 1991 Winter Park, Florida BK Mladá Boleslav (ELH)
35 G Leggio, DavidDavid Leggio 6 ft 0 in (183 cm) 7001839145884500000♠185 lb (84 kg) 000000001984-07-31-0000July 31, 1984 Williamsville, New York EHC Red Bull München (DEL)
42 F Kolarik, ChadChad Kolarik 5 ft 11 in (180 cm) 7001830074037100000♠183 lb (83 kg) 000000001986-01-26-0000January 26, 1986 Abington, Pennsylvania Adler Mannheim (DEL)
94 F Stoa, RyanRyan Stoa 6 ft 3 in (191 cm) 7001961615824400000♠212 lb (96 kg) 000000001987-04-13-0000April 13, 1987 Bloomington, Minnesota HC Spartak Moscow (KHL)
97 D Gilroy, MattMatt Gilroy – A 6 ft 2 in (188 cm) 7001920792511100000♠203 lb (92 kg) 000000001984-07-30-0000July 30, 1984 North Bellmore, New York Jokerit (KHL)
IIHF World Championship directorate awards[edit]
Main article: List of IIHF World Championship directorate award winners
The IIHF has given awards for each year's championship tournament to the top goalie, defenseman, and forward (all since 1954), and most valuable player (since 2004). The following USA team members have won awards.
1955 – Don Rigazio (goalie)
1956 – Willard Ikola (goalie)
1959 – Bill Cleary (forward)
1960 – Jack McCartan (goalie)
1962 – John Mayasich (defenseman)
1967 – Carl Wetzel (goalie)
2004 – Ty Conklin (goalie)
2014 – Seth Jones (defenseman)
See also[edit]
List of United States national hockey team rosters
References[edit]
Jump up ^ World Ranking
Jump up ^ [1]
Jump up ^ "NHL announces World Cup of Hockey for 2016". The Canadian Press. 2015-01-24. Retrieved January 29, 2015.
Jump up ^ http://www.usahockey.com/page/show/839306-membership-statistics
Jump up ^ http://unitedstatesofhockey.com/2014/06/17/u-s-hockey-participation-numbers-for-2013-14/
Jump up ^ Burnside, Scott (2010-02-08). "Hockey's miracle before the 'Miracle'". ESPN. Retrieved 2011-10-07.
Jump up ^ "The Morning Skate: The Forgotten Miracle of 1960". New York Times. 2009-12-11. Retrieved 2011-10-07.
Jump up ^ "Hockey Game Seen by 27.6 Million" New York Times, 1 March 2010. Retrieved 1 March 2010
Jump up ^ https://www.teamusa.org/News/2018/January/01/New-Look-2018-US-Olympic-Mens-Ice-Hockey-Team-Named-Led-By-2006-Olympian-Brian-Gionta
Jump up ^ See: Ice Hockey World Championships.
Jump up ^ See Ice Hockey World Championships#1930–1953: Canadian dominance. World War II forced the cancellation of the 1940 and 1944 Winter Olympics and the world championships from 1941 to 1946. "International hockey timeline". International Ice Hockey Federation. Retrieved 2009-03-10. (ed.) Carl Diem (January 1940). "The Fifth Olympic Winter Games Will Not Be Held" (PDF). Olympic Review. Berlin: International Olympic Institute (8): 8–10. Retrieved 2009-03-10. CS1 maint: Extra text: authors list (link)
Jump up ^ See: 1972 World Ice Hockey Championships. For the first time, a separate tournament is held for both the World Championships and the Winter Olympics.
^ Jump up to: a b c No championships were held during the Olympic years 1980, 1984, and 1988. See: Ice Hockey World Championships#1976–1987: First years of open competition and List of IIHF World Championship medalists.
Jump up ^ USA Hockey Deutschland Cup Archives Archived October 4, 2006, at the Wayback Machine.
Jump up ^ 2003&2004 Deutschland Cup
Jump up ^ 2005 Deutschland Cup
Jump up ^ USA Hockey Deutschland/TUI Cup results Archived October 4, 2006, at the Wayback Machine.
Jump up ^ "USA Hockey Announces Rosters For 2018 Olympic/Paralympic Teams". USAHockey.com. January 1, 2018. Retrieved January 3, 2018.
Jump up ^ Team Roster United States
External links[edit]
Wikimedia Commons has media related to United States national ice hockey team.
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value: Boston University Terriers men's ice hockey

value: St. Cloud State Huskies men's ice hockey

value: Abington Township, Montgomery County, Pennsylvania

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value: Edit section: IIHF World Championship directorate awards

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value: List of United States national hockey team rosters

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value: Template:Ice hockey in the United States

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value: United States men's national junior ice hockey team

value: USA Hockey National Team Development Program

value: United States men's national under-16 ice hockey team

value: United States women's national ice hockey team

value: United States women's national under-18 ice hockey team

value: United States men's national ice sledge hockey team

value: National Collegiate Athletic Association

value: NCAA Division I Men's Ice Hockey Championship

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value: National Collegiate women's ice hockey championship

value: Rocky Mountain Junior Hockey League (2015–)

value: List of Olympic men's ice hockey players for the United States

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value: United States national American football team

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value: United States men's national Australian rules football team

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value: United States men's national ball hockey team

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value: United States women's national bandy team

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value: United States national under-17 baseball team (page does not exist)

value: United States women's national baseball team

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value: United States men's national under-18 basketball team

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value: United States men's national wheelchair basketball team

value: United States women's national wheelchair basketball team (page does not exist)

value: United States women's national cricket team

value: United States men's national field hockey team

value: United States women's national field hockey team

value: United States women's national floorball team

value: United States women's national goalball team

value: United States women's junior national goalball team

value: United States men's national gymnastics team

value: United States men's under-18 national gymnastics team (page does not exist)

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value: United States women's national gymnastics team

value: United States women's national junior gymnastics team (page does not exist)

value: United States women's national handball team

value: United States women's national under-22 ice hockey team (page does not exist)

value: United States women's national ice sledge hockey team

value: United States men's national inline hockey team

value: United States women's national inline hockey team

value: United States men's national lacrosse team

value: United States men's national under-19 lacrosse team (page does not exist)

value: United States women's national lacrosse team

value: United States women's national under-19 lacrosse team (page does not exist)

value: United States national indoor lacrosse team

value:
/wiki/Team_USA_(roller_derby)
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Team USA (roller derby)

value: United States national roller hockey team

value: United States women's national roller hockey team (page does not exist)

value: United States national rugby league team

value: United States national under-20 rugby union team

value: United States national rugby sevens team

value: United States women's national rugby union team

value: United States women's national rugby sevens team

value: United States men's national soccer team

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value: United States men's national under-20 soccer team

value: United States men's national under-18 soccer team

value: United States men's national under-17 soccer team

value: United States men's national under-16 soccer team (page does not exist)

value: United States women's national soccer team

value: United States women's national under-23 soccer team

value: United States women's national under-20 soccer team

value: United States women's national under-18 soccer team

value: United States women's national under-17 soccer team

value: United States women's national under-16 soccer team (page does not exist)

value: United States national beach soccer team

value: United States men's Paralympic soccer team

value: United States national cerebral palsy football team

value: United States men's national softball team

value: United States men's junior national softball team

value: United States men's national slowpitch softball team (page does not exist)

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value: United States women's junior national softball team

value: United States women's national slowpitch softball team (page does not exist)

value: United States men's national squash team

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value: United States men's national volleyball team

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value: United States women's national under-18 volleyball team

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value: South Africa men's national ice hockey team

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value: North Korea men's national ice hockey team

value: South Korea men's national ice hockey team

value: Kyrgyzstan men's national ice hockey team

value: New Zealand men's national ice hockey team

value: Philippines men's national ice hockey team

value: Saudi Arabia men's national ice hockey team

value: Singapore men's national ice hockey team

value: Turkmenistan men's national ice hockey team

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value: Bosnia and Herzegovina men's national ice hockey team

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value: Luxembourg men's national ice hockey team

value: Macedonia men's national ice hockey team

value: Netherlands men's national ice hockey team

value: Protectorate of Bohemia and Moravia men's national ice hockey team

value: Saint Pierre and Miquelon men's national ice hockey team

value: Serbia and Montenegro men's national ice hockey team

value: Category:National ice hockey teams in the Americas

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value: Зборная ЗША па хакеі з шайбай – Belarusian

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value: Мужчынская зборная ЗША па хакеі – Belarusian (Taraškievica orthography)

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value: Eishockeynationalmannschaft der Vereinigten Staaten – German

value: Selección de hockey sobre hielo de Estados Unidos – Spanish

value: Équipe des États-Unis de hockey sur glace – French

value: 미국 아이스하키 국가대표팀 – Korean

value: Reprezentacija SAD-a u hokeju na ledu – Croatian

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value: Nazionale di hockey su ghiaccio maschile degli Stati Uniti d'America – Italian

value: נבחרת ארצות הברית בהוקי קרח – Hebrew

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value: JAV vyrų ledo ritulio rinktinė – Lithuanian

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value: アイスホッケーアメリカ合衆国代表 – Japanese

value: USAs herrelandslag i ishockey – Norwegian

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value: Reprezentacja Stanów Zjednoczonych w hokeju na lodzie mężczyzn – Polish

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value: Seleção dos Estados Unidos de Hóquei no Gelo – Portuguese

value: Сборная США по хоккею с шайбой – Russian

value: United States men's national ice hockey team – Simple English

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value: Репрезентација Сједињених Америчких Држава у хокеју на леду – Serbian

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