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Type I and type II errors
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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 rejection of a true null hypothesis (also known as a "false positive" finding), while a type II error is failing to reject 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
7 See also
8 Footnotes
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. A test statistic is robust if the Type I error rate is controlled.[4]
These terms are also used in a more general way by social scientists and others to refer to flaws in reasoning.[5] 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).
In terms of folk tales, an investigator may see the wolf when there is none ("raising a false alarm"). Where the null hypothesis, H0, is: no wolf.
The type I error rate or significance level is the probability of rejecting the null hypothesis given that it is true.[6][7] 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.[6]
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.[8]
In terms of folk tales, an investigator may fail to see the wolf when it is present ("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)
Fail to
reject

Correct inference
(True Negatives)

Type II error
(False Negative)
Reject

Type I error
(False Positive)

Correct inference
(True Positives)
Examples[edit]
Example 1[edit]
Hypothesis: "Adding water to toothpaste protects against cavities."
Null hypothesis (H0): "Adding water does not make toothpaste more effective in fighting 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 does not make it more effective in protecting against cavities), but this null hypothesis is rejected based on bad experimental data or an extreme outcome of chance alone.
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"[9]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".[10]
They identified "two sources of error", namely:
(a) the error of rejecting a hypothesis that should have not been rejected, and
(b) the error of failing to reject a hypothesis that should have been rejected.[9]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.[11]
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 fail to reject, 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 fail to reject H0 when some alternative hypothesis HA or H1 is true.[12]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[12]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."[13] 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).[14]
If the system is designed to rarely match suspects 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.[15]
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.
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
Footnotes[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 ^ Derrick, B; Toher, D; White, P (2016). "Why Welchs test is Type I error robust". The Quantitative Methods for Psychology. 12 (1): 30–38. doi:10.20982/tqmp.12.1.p030.
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 Fail to reject (Type II error) are approximately equal) is .00076%
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])
References[edit]
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). [2]
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.
External links[edit]
Bias and Confounding – presentation by Nigel Paneth, Graduate School of Public Health, University of Pittsburgh
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Susan Barlow
From Wikipedia, the free encyclopedia
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Susan Barlow
Coronation Street character
Portrayed by
Katie Heannau (1965–1970)
Wendy Jane Walker (1970–1974, 1985–1987)
Suzy Patterson (1979–1981)
Joanna Foster (2001)
Duration
1965–1971, 1973–1974, 1979–1981, 1985–1987, 2001
First appearance
Episode 450
5 April 1965
Last appearance
Episode 4983
11 February 2001
Introduced by
H.V. Kershaw (1965)
Eric Prytherch (1973, 1974)
Bill Podmore (1979, 1981)
John G. Temple (1985)
Jane MacNaught (2001)
Book appearances
Coronation Street:
The Complete Saga
Classification
Former; regular
Profile
Other names
Susan Baldwin (Married name)
[show]Family
Father
Ken Barlow
Mother
Valerie Barlow
Stepmother
Janet Barlow (1976-1979)
Deirdre Barlow (1981-1992)
Brothers
Peter Barlow
Half-brothers
Daniel Osbourne
Lawrence Cunningham
Half-sisters
Tracy Barlow (adoptive)
Husband
Mike Baldwin (1986–1988)
Sons
Adam Barlow
Grandfathers
Frank Barlow
Alfred Tatlock
Grandmothers
Ida Barlow
Edith Tatlock
Uncles
David Barlow
Aunts
Joyce Tatlock
Nephews
James Cunningham
Simon Barlow
Nieces
Chloe Cunningham
First cousins
Darren Barlow
Other relatives
Nancy Leathers
Albert Tatlock
Ada Broadbent
Susan Ida Barlow (also Baldwin) was a fictional character from the British ITV soap opera, Coronation Street. She made her debut screen appearance on 5 April 1965. Susan has been portrayed by four actresses since her introduction. Katie Heannau was the first actress to the character. Wendy Jane Walker took over the role for two years from 1970. When the character returned in 1979, Suzy Patterson was recast in the role. She remained with the show until 1981, in 1985 Walker reprised the role until 1987. In 2001, Susan returned with Joanna Foster in the role for a month until the character was killed-off.
Contents
[hide]
1 Storylines
2 Casting
3 Development
4 Reception
5 References
Storylines[edit]
Susan was the daughter of Ken and Valerie Barlow and twin sister of Peter Barlow. She was born on 5 April 1965 after Len Fairclough drove Valerie to the hospital in his van. Most of Susan's early storylines were alongside her twin brother, Peter.
Valerie's life changed completely as her time was spent looking after the twins and Ken. The Barlows had many friends and could easily find babysitters, allowing Valerie to attend evening classes in Sociology. In November 1965, Ken was looking after the twins but when he visited The Rovers Return briefly, a piece of coal fell from the fire and filled the house with smoke. Valerie came home before Ken put the fire out but was outraged that he had left the babies alone while he went to buy cigarettes. Valerie told Ken she would leave him if he ever smoked again.
In 1966, Ken had a fling with reporter, Jackie Marsh. Valerie left him briefly and took the twins to Glasgow but returned when he convinced her that he would be a more devoted husband and father from then on.
In 1968, the Mission of Glad Tidings and Elliston's Raincoat Factory across the Street were torn down to make way for a block of maisonettes. As they were being demolished, Ken and Valerie realised the twins were missing. They were found in the factory just before the wrecking ball hit.
In 1969, Susan's maternal grandmother, Edith, stayed with the Barlows. When Edith took the twins to the fair, she lost them but they were found by an elderly woman who had taken them in and not contacted the family.
In January 1971, Ken was offered a teaching position in Jamaica and Valerie agreed to emigrate with the twins. However, Valerie died when she was electrocuted by a faulty hairdryer. Later in the year, Peter and Susan were sent to live in Scotland with their maternal grandparents after Ken struggled to make suitable childcare arrangements and continue to work as a teacher. Susan came back to Weatherfield to visit her father in 1973 and 1974.
Susan returned to Coronation Street again for New Year's Eve 1979, and stayed with her father and his girlfriend Deirdre Langton into the New Year. Susan then went travelling and visited Ken again in April 1980, along with her boyfriend Craig Brennan, who Ken did not approve of. Susan visited again for Ken and Deirdre's wedding in July 1981. Susan went back to Scotland, and decided to attend university.
In 1985, Susan returned again now working as a market researcher. She began an affair with her father's sworn enemy Mike Baldwin and they got engaged but Ken refused to attend as he hated Mike and could not come to terms with the fact he was marrying his little girl. But after a lot of thinking and talking to Susan, Peter and Deirdre he walked Susan down the aisle on 6 May 1986. The marriage failed, because Susan's career ambitions did not fit in with Mike's plans for the future. When she got pregnant, Mike wanted her to abandon her business; Susan decided to have an abortion. Mike branded her a killer and a murdering bitch and she left him and moved to Newcastle after just a year of marriage in November 1987.
By December 2000, Peter was living with Ken and Deirdre. While arguing with Ken, Peter let slip that Susan had a son. Ken insisted Peter explain, which he did, telling Ken that Susan had lied about the abortion and gave birth to her son, Adam in May 1988. Susan had only told Peter this. Ken went to Scotland the following month to visit Susan and she made him swear not to tell Mike. Ken told Deirdre and she told her ex-lover Dev Alahan. He told Mike who called Susan and demanded access. Susan, who was visiting her father and brother, refused. Mike threatened her with legal action. Susan tried to flee again with Adam to Ireland, but while driving on the M6, her car crashed into a tree. Susan was killed instantly but Adam suffered only minor injuries. Susan's body was taken back to Weatherfield for her funeral, and she was buried next to her mother, Valerie.
Casting[edit]
Katie Heannau played Susan as a baby and young child from 1965 until 1970 when Heannau and her family moved away from the Manchester area.[1] Child actress Wendy Jane Walker took over the role later in 1970. Although Susan was written out in 1971, Walker made recurring appearances in 1973 and 1974. Suzy Patterson played Susan from 1979 to 1981 but only made a small number of appearances. Walker took back the role in 1985 and Susan became a main character again before departing in 1987. Patterson had played the role with a strong Scots accent, whereas Walker did not. After a thirteen-year absence, Susan returned for a final time in early 2001, now played by Joanna Foster, which saw the character get killed off after introducing her secret son Adam.
Development[edit]
In January 2001, the character returned to Coronation Street with actress Joanna Foster taking over the role.[2] Susan comes back to Weatherfield to introduce her son, Adam (Iain de Caestecker) to his grandfather, Ken (William Roache).[3] Rick Fulton of the Daily Record said Ken is "amazed, but also devastated" that Susan kept his grandson a secret for so long.[3] Adam's father is Mike Baldwin (Johnny Briggs), who Susan married in 1986.[3] Susan told Mike she had an abortion and he threw her out the following year. Fulton explained "But that will be exposed as a lie. It seems Susan, who had grown to hate her husband, didn't think Mike would be a fit father for her child. She decided she wanted to bring up the baby on her own without any of his influence."[3] The news rocks Ken and Mike, when they learn they are linked by Adam.[3] On 22 January 2001, Brian Roberts of the Daily Mirror reported Susan would be killed off in a car accident.[2] Her death would then kick-start a custody battle for her son, Adam, between her father and her ex-husband.[2] Roberts said Susan is "adamant" that she does not want Mike having anything to do with Adam and she decides to take him to Ireland. However, while they are on their way Susan crashes the car and dies.[2]
Reception[edit]
Johnny Briggs disapproved of the storyline that saw his character (Mike Baldwin) marry Susan as he thought it would tie his character down.[4] He also did not get on with Walker and was pleased when the relationship broke up.[4] The Daily Mirror's Charlie Catchpole described Susan as the "hapless" wife and daughter of Mike and Ken.[5] He commented on her 2001 recast saying "I don't know why either of them believed a word she said, since Susan had been blonde and blue-eyed, and this impostor had black hair and brown eyes."[5] Catchpole added that Susan's off screen death was one of the soap world's cruelest exits.[5]
References[edit]
Jump up ^ "TV Talk: It's out! The Street's twin secret". TVTimes. 9 May 1970.
^ Jump up to: a b c d Roberts, Brian (22 January 2001). "Custody Street; Ken and Mike fight for orphan Adam". Daily Mirror. Trinity Mirror. Retrieved 3 March 2012.
^ Jump up to: a b c d e Fulton, Rick (1 January 2001). "I can't believe millions will be watching my Street debut". Daily Record. Trinity Mirror. Retrieved 3 March 2012.
^ Jump up to: a b Donnelly, Claire (22 March 2006). "Mike's top 20 moments". Daily Record. (Trinity Mirror). Archived from the original on 3 May 2011. Retrieved 30 April 2011.
^ Jump up to: a b c Catchpole, Charlie (13 February 2001). "Susan is no dead ringer". Daily Mirror. Trinity Mirror. Retrieved 3 March 2012.
[show]
v
t
e
Coronation Street characters
[show]
Present and future characters
Ken Barlow
Rita Sullivan
Peter Barlow
Gail McIntyre
Tracy Barlow
Audrey Roberts
Kevin Webster
Jenny Bradley
Sally Webster
Gina Seddon
Sarah Platt
Liz McDonald
Steve McDonald
Rosie Webster
David Platt
Norris Cole
Sophie Webster
Daniel Osbourne
Roy Cropper
Robert Preston
Leanne Battersby
Toyah Battersby
Tyrone Dobbs
Dev Alahan
Eileen Grimshaw
Maria Connor
Kirk Sutherland
Bethany Platt
Adam Barlow
Fiz Brown
Simon Barlow
Sean Tully
Chesney Brown
Amy Barlow
Aadi Alahan
Asha Alahan
Michelle Connor
Carla Connor
Alex Neeson
Gary Windass
Mary Taylor
Liam Connor Jr.
Izzy Armstrong
Max Turner
Jack Webster
Hope Stape
Faye Windass
Eva Price
Beth Tinker
Craig Tinker
Joseph Brown
Ruby Dobbs
Tim Metcalfe
Steph Britton
Sinead Tinker
Jake Windass
Lily Platt
Pat Phelan
Alya Nazir
Yasmeen Nazir
Dr. Susan Gaddas
Zeedan Nazir
Gemma Winter
Billy Mayhew
Cathy Matthews
Aidan Connor
Alex Warner
Johnny Connor
Kate Connor
Rana Habeeb
Harry Platt
Seb Franklin
Shona Ramsey
Jude Appleton
Oliver Battersby
Nicola Rubinstein
Summer Spellman
Angie Appleton
George Appleton
Abi Franklin
Imran Habeeb
Henry Newton
[hide]
Past characters
Sunita Alahan
Christine Appleby
Lewis Archer
Shirley Armitage
Katy Armstrong
Owen Armstrong
Danny Baldwin
Frankie Baldwin
Jamie Baldwin
Mike Baldwin
Deirdre Barlow
Frank Barlow
Ida Barlow
Susan Barlow
Des Barnes
Natalie Barnes
Janice Battersby
Les Battersby
Cilla Battersby-Brown
Sheila Birtles
Emily Bishop
Natasha Blakeman
Alan Bradley
Renee Bradshaw
Teresa Bryant
Julie Carp
Casey Carswell
Matt Carter
Paul Clayton
Liam Connor
Paul Connor
Ryan Connor
Kelly Crabtree
Hayley Cropper
Trevor Dean
Marcus Dent
Jackie Dobbs
Molly Dobbs
Rob Donovan
Jack Duckworth
Terry Duckworth
Tommy Duckworth
Vera Duckworth
Fred Elliott
Anne Foster
Frank Foster
Alec Gilroy
Tony Gordon
Cheryl Gray
Jason Grimshaw
Todd Grimshaw
Alma Halliwell
Richard Hillman
Charlotte Hoyle
Blanche Hunt
Mel Hutchwright
Amber Kalirai
Ray Langton
Bet Lynch
Tara Mandal
Ciaran McCarthy
Becky McDonald
Jim McDonald
Karen McDonald
Joe McIntyre
Tina McIntyre
Darryl Morton
Jerry Morton
Kayleigh Morton
Mel Morton
Lloyd Mullaney
Karl Munro
Hilda Ogden
Stan Ogden
Ashley Peacock
Claire Peacock
Kylie Platt
Martin Platt
Sian Powers
Stella Price
Graeme Proctor
Mark Redman
Alf Roberts
Maya Sharma
Ena Sharples
Kirsty Soames
John Stape
Jed Stone
Candice Stowe
Luke Strong
Charlie Stubbs
Percy Sugden
Leonard Swindley
Dennis Tanner
Elsie Tanner
Albert Tatlock
Ivy Tilsley
Nick Tilsley
Vernon Tomlin
Shelley Unwin
Annie Walker
Curly Watts
Raquel Watts
Bill Webster
Betty Williams
Violet Wilson
Derek Wilton
Mavis Wilton
Anna Windass
Eddie Windass
Eddie Yeats
[show]
Lists of characters
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Retrieved from "https://en.wikipedia.org/w/index.php?title=Susan_Barlow&oldid=820237984"
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Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. By using this site, you agree to the Terms of Use and Privacy Policy. Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization.
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List of Yuri on Ice characters
From Wikipedia, the free encyclopedia
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This is a list of primary characters from the anime series Yuri on Ice. Most of them are characters taking part in the ISU Grand Prix of Figure Skating championship, with the central figures being Japanese figure skater Yuri Katsuki, his Russian coach Victor Nikiforov, and Yuri's Russian rival skater Yuri Plisetsky.
Contents
[hide]
1 Main characters
1.1 Yuri Katsuki
1.2 Victor Nikiforov
1.3 Yuri Plisetsky
2 Supporting characters
2.1 Katsuki family
2.2 Nishigōri family
2.3 Coaches and other characters
2.4 International skaters
3 Cameo appearances
4 References
Main characters[edit]
Yuri Katsuki[edit]
Yuri Katsuki (勝生勇利, Katsuki Yūri)
Voiced by: Toshiyuki Toyonaga[1][2] (Japanese); Josh Grelle (English)[3]
The main protagonist, Yuri is a 23-year-old[4] (24 after episode 9) figure skater. Yuri began skating at a very young age and aspires to become a great figure skater like his idol, Victor Nikiforov. As stated by his teacher Minako, Yuri is not a prodigy at skating, but his skills were honed by rigorous training, which also grants him good stamina. He is Japan's top figure skater; his greatest skating assets being his peculiar sense of rhythm and unique step. However, Yuri succumbs to pressure easily, causing him to mess up at crucial moments. After failing his first appearance at the Grand Prix Final, his self-confidence was greatly affected, succumbing to extreme anxiety due to his shortcomings and performances in following competitions, thus leaving a black mark on his career, and resulting in his returning home after being away for 5 years.
Yuri is kind, sensitive, and mild-mannered; he is also introverted and has a hard time opening himself up to others, preferring to be alone and practice ballet or ice skating than socializing with his peers. Despite having a lot of support from his family and friends, he greatly lacks self-esteem and is easily discouraged, causing bouts of anxiety. Contrary to how Yuri sees himself due to his past failures, he is still considered Japan's best skater, having the loyal support of his fans and the juniors skaters that look up to him, much to his bewilderment. Under Victor's tutelage, Yuri slowly becomes more self-assured and confident, aiming to reinvent himself and putting more input into his programs as he strives to win the 2016-2017 Grand Prix series[5].
Victor Nikiforov[edit]
Victor Nikiforov (ヴィクトル・ニキフォロフ, Vikutoru Nikiforofu)
Voiced by: Junichi Suwabe[1][2] (Japanese); Jerry Jewell (English)[3]
Victor is a 27-year-old[4] figure skating genius and living legend from Russia who Yuri K. has idolized since he was a child. Starting as a teenager, Victor has won numerous competitions, including winning five consecutive Grand Prix Finals, following a string of victories in Nationals and the European Figure Skating Championships. At the start of the series, Victor wins the 2015-2016 World Championships marking him winning it consecutively five times and people begin to question what he will do next season, as he is approaching the retirement age for figure skaters. Victor's personal motto is to "Do the opposite of what people expect, that is the only way you will surprise them!". He shows this through his skating programs as he creates his own choreography and music. However, he lost his inspiration to make new routines because he knows people have grown used to his record-breaking talent. After seeing Yuri K. skating his winning routine in the viral video, Victor regains his inspiration and decides to become Yuri K.'s coach to unlock his full potential.
As the top skater in the world, Victor is very popular and charismatic, as well as being flirtatious and liking to speak his mind. As stated by Yakov, Victor does whatever he wants and often gives off a whimsical, carefree attitude. When he needs to be, Victor can be serious and contemplative, especially in regards to skating. As a coach, Victor is very patient and supportive, yet he challenges and helps push Yuri K. into exploring his untapped potential and charm.
Viktor's face is modeled after that of American actor John Cameron Mitchell. Mitchell is most known as the book writer and original star of Hedwig and the Angry Inch, which series creator Mitsuro Kubo had seen on Broadway with Mitchell in the lead while she was deciding on Viktor's appearance.[6]
Victor also pays homage to real life skater Johnny Weir in episode 7 with his costume and rose crown.
Yuri Plisetsky[edit]
Yuri Plisetsky (ユーリ・プリセツキー, Yūri Purisetsukī)
Voiced by: Kōki Uchiyama[1][2] (Japanese); Micah Solusod (English)[3]
Yuri is a 15-year-old[4] Russian figure skater and a rink mate to Victor Nikiforov. Yuri has been a figure skating prodigy since he was young and has won two consecutive Junior Grand Prix Final and Junior World Championships. He won first place in the junior circuit during the year Yuri K. lost in the 2015-2016 Grand Prix Final and starts to make his senior debut during the 2016-2017[7] season. Due to the fact that he surpasses his peer group, Yuri is arrogant and his harsh personality earned him the nicknamed "Russian Punk," though he is also known as the "Russian Fairy" because of his good looks. During his stay in Japan, he is given the nickname "Yurio" by Mari Katsuki to differentiate him from Yuri K.
Although Yuri is typically crude and short-tempered, he also shows a kind side, though he shows it in an abrasive way. He has a strong fondness for cats and any feline-related merchandise. Yuri is very ambitious wants to make the most of his skating career while he can, being at a precarious age where his abilities will dampen as his body matures.
Supporting characters[edit]
Katsuki family[edit]
Toshiya Katsuki (勝生 利也, Katsuki Toshiya)
Voiced by: Soryu Konno (Japanese); Charles Campbell (English)[8]
Toshiya is the 54-year-old father of Yuri Katsuki and Mari, and the owner of Yu-topia Akatsuki, the hot springs resort that his family runs together.
Hiroko Katsuki (勝生 寛子, Katsuki Hiroko)
Voiced by: Kei Hayami (Japanese); Rachel Robinson (English)[8]
Hiroko is the 49-year-old mother of Yuri and Mari, and Toshiya's wife. She is kind and welcoming and helps runs Yu-topia with the rest of her family.
Mari Katsuki (勝生 真利, Katsuki Mari)
Voiced by: Kyōko Sakai (Japanese); Leah Clark (English)[8]
Mari is Yuri Katsuki's 30-year-old sister and helps run Yu-topia alongside the rest of her family. During Yuri P.'s visit to Japan, she was the one who nicknamed him "Yurio", due to his resemblance to a boyband member she likes, in order to distinguish between him and her brother.
Nishigōri family[edit]
Yuko Nishigōri (西郡 優子, Nishigōri Yūko)
Voiced by: Mariya Ise (Japanese); Alexis Tipton (English)[3]
A 25-year-old woman, Yuko is Yuri K.'s best friend, and former rink mate. She has always treated Yuri with kindness and offers him encouragement about his skating career. Yuko is the one who first showed Victor's skating performances to Yuri, and the two of them greatly admired him from a young age, regularly copying his routines during their practices. Yuri pulls himself out of his slump by practicing Victor's latest routine so he can show it to Yuko when he returns to Kyushu. Yuko is married to another mutual childhood friend and ex-rink mate of Yuri's, Takeshi, and they have triplet daughters. Yuko and her family run the local ice rink, Ice Castle Hasetsu, where she and her friends learned to skate as children.
Takeshi Nishigōri (西郡 豪, Nishigōri Takeshi)
Voiced by: Jun Fukuyama[9] (Japanese); Cris George (English)[8]
A 24-year-old man, Takeshi is Yuri K.'s childhood friend and ex-rink mate. He is married to Yuko, another childhood friend that skated with him and Yuri, and is the father of Axel, Lutz, and Loop. He helps his wife run the local ice rink, Ice Castle Hasetsu, where the three learned to skate as children. Although he has a history of teasing Yuri when they were children, Takeshi is very supportive of Yuri, and often gives practical, blunt advice to him.
Axel Nishigōri (西郡 空挧流, Nishigōri Akuseru) Lutz Nishigōri (西郡 流譜, Nishigōri Ruttsu) Loop Nishigōri (西郡 流麗, Nishigōri Rūpu)
Voiced by: Akiko Yajima (Japanese); Lara Woodhull (Axel), Kristi Rothrock (Lutz), Kristen McGuire (Loop) (English)[8]
Axel, Lutz, and Loop are the six-year-old triplet daughters of Takeshi and Yuko, and each of them are named after a type of figure skating jump (Axel jump, Lutz jump, and Loop jump). They are big fans of figure skating and very active on social media, with their Instagram being "sukeota3sisters". They are very mischievous and are the ones responsible for secretly recording and posting Yuri K.'s performance of Victor's routine on the Internet via their mother's YouTube account without permission. They can be told apart by their hairstyles and color-coded outfits; Axel has pigtails and wears purple, Lutz has a bun and wears light blue, and Loop has a ponytail and wears pink.
Coaches and other characters[edit]
Minako Okukawa (奥川 ミナコ, Okukawa Minako)
Voiced by: Yuka Komatsu (Japanese); Colleen Clinkenbeard (English)[3]
Minako is Yuri Katsuki's ballet teacher and owner of the local dance studio in Hasetsu. She is currently lacking students, aside for Yuri K., who she still helps in his training. She was once a famous dancer herself and a teacher that traveled around the world to teach others the art of ballet. Minako also runs a small snack bar called Kachu.
Hisashi Morooka (諸岡 久志, Morooka Hisashi)
Voiced by: Taihei Katō (Japanese); Sonny Strait (English)[3]
Morooka is a 27-year-old figure skating enthusiast and an announcer at many of the competitions. He's a fan of Yuri K. and encourages him to continue with his career after his defeat in his first Grand Prix Final.
Yakov Feltsman (ヤコフ・フェルツマン, Yakofu Ferutsuman)
Voiced by: Hideaki Tezuka (Japanese); Daman Mills (English)[8]
A 70-year-old man, Yakov is Yuri Plisetsky's, Georgi Popovich's, Mila Babicheva's, and formerly Victor Nikiforov's figure skating coach. A no-nonsense man that is strict with his students, Yakov is easily angered by Victor's whimsical behavior, including his choice to leave Russia to train Yuri K., and Yuri P.'s dismissive attitude towards his advice. However, he is a good coach who is very fond of his students and wishes for each of them reach their full potential.
Lilia Baranovskaya (リリア・バラノフスカヤ, Riria Baranofusukaya)
Voiced by: Seiko Tamura (Japanese); Caitlin Glass (English)[3]
A stern and strict woman, Lilia is a former Prima ballerina from Bolshoi Ballet and Yakov's ex-wife, with whom she has a professional relationship (However, it might be implied that Yakov still has some feelings for her). After Yuri P.'s return to Russia following his loss to Yuri K., Yakov asks Lilia to train Yuri P. in ballet to help him improve his skills. She is currently housing Yuri P. at her home.
Celestino Cialdini (チェレスティーノ・チャルディーニ, Cheresutīno Charudīni)
Voiced by: Kōji Ochiai (Japanese); Ian Sinclair (English)[3]
A 45-year-old Italian-American, Celestino is Yuri K.'s former figure skating coach from when he was training at the Detroit Skating Club, and Phichit Chulanont's current coach. After Yuri's loss at his first Grand Prix Final, Yuri ended his contract with Celestino and returned home to Japan. When he heard Victor took Yuri as his pupil, Celestino shows no ill-will towards this development and encourages Yuri throughout his second Grand Prix series. A good and understanding coach to his current and former pupils, Celestino takes pride seeing them succeed. He is affectionately nicknamed "Ciao Ciao" by Phichit, due to using the words frequently to greet other people.
Nikolai "Kolya" Plisetsky (ニコライ "コーリャ"・プリセツキー, Nikorai "Kōrya" Purisetsukī)
Voiced by: Misao Kobayashi
Kolya is Yuri Plisetsky's grandfather who lives in Moscow and the only person that Yuri that shows his soft side to. Kolya is very loving and supportive towards his grandson and used to accompany him to his skating lessons when he was younger.
Kanako Odagaki (小田垣 香奈子, Odagaki Kanako)
Voiced by: Yuki Kazu[10] (Japanese); Bryn Apprill (English)
Kanako is Kenjirō Minami's figuring skating coach and trains at the Hakata Skate Club with him. She acts motherly towards Minami and is shown to be rather indulgent of his enthusiasm.
Josef Karpisek (ヨゼフ・カルピーシェク, Yozefu Karupīsheku)
Voiced by: Tomisaburou Horikoshi[11]
Josef is Christophe Giacometti's coach, being with him since he was young, and understands his student's habits well, such as letting Christophe go at his own pace in competitions.
Alain Leroy (アラン・ルロワ, Aran Rurowa)
Voiced by: Atsuo Hasegawa[12]
Alain is Jean-Jacques Leroy's father, coach, and a former Ice Dance Olympic Champion alongside his wife.
Nathalie Leroy (ナタリー・ルロワ, Natarii Rurowa)
Voiced by: Kujira[12]
Nathalie is Jean-Jacques Leroy's mother, coach, and a former Ice Dance Olympic Champion alongside her husband.
Isabella Yang (イザベラ・ヤン, Izabera Yan)
Voiced by: Yuki Kazu[13] (Japanese); Dawn M. Bennett[14] (English)
Isabella is Jean-Jacques Leroy's supportive and loyal fiancée and a member of his fan club, JJ Girls.
Min-soo Park (박민수, パク・ミンソ, Paku Minso)
Voiced by: Miyako Chida[15]
Min-soo is Seung Gil Lee's coach, though she and her student doesn't seem very close.
International skaters[edit]
Otabek Altin (オタベック・アルティン, Otabekku Arutin)
Voiced by: Yoshimasa Hosoya[16][17] (Japanese); Mike McFarland[18] (English)
An 18-year-old figure skater from Kazakhstan, Otabek is very popular in his country and seeks to make them proud with his skating. A private individual, Otabek is reticent and stoic, but is straightforward and earnest in his words and actions. A skilled skater, Otabek launched his senior debut by winning third place in the latest World Championships where Victor won his fifth gold medal. Five years prior to series, Otabek attended one of Yakov's training camps and came to respect Yuri P., feeling kinship with the younger boy and befriends him years later.
Guang-Hong Ji (季光虹 ジ・グァンホン, Ji Gwanhon)[4]
Voiced by: Yūtarō Honjō[16][17] (Japanese); Howard Wang[3][19] (English)
A 17-year-old figure skater from China, Guang-Hong dreams of being a Hollywood celebrity and trains in Canada during the summer. He has a sweet and shy personality that makes him soft-spoken and easily flustered. Guang-Hong is good friends with Phichit Chulanont, who he admires and who is credited with getting him into SNS, and Leo de la Iglesia.
Christophe "Chris" Giacometti (クリストフ・ジャコメッティ, Kurisutofu Jakometti)
Voiced by: Hiroki Yasumoto[16][17] (Japanese); Christopher Sabat[3] (English)
A 25-year-old figure skater from Switzerland. Christophe has a healthy rivalry with Victor, admiring him since he was young, and won second place in the latest World Championships where Victor won his fifth gold medal. Typically easy-going, Chris is a slow starter during competitions and usually does better later in the free program, and is known for his sex appeal. With Victor taking a season off to coach Yuri K., Chris initially believes he won't have any real competition, but changes his mind when he sees how Yuri K. has improved and is determines to beat him in order to get Victor to compete again.
Jean-Jacques "JJ" Leroy (ジャン・ジャック・ルロワ, Jan Jakku Rurowa)
Voiced by: Mamoru Miyano[16][17] (Japanese); Jeremy Inman (English)[3][20]
A 19-year-old figure skater from Canada, Jean-Jacques, or "JJ", comes from a family of figure skaters. His parents were Olympic ice dance champions, who are also his coaches, and his younger brother and sister both participate in the junior league. A very skilled skater, JJ has a narcissistic attitude, tends to say whatever is on his mind, and enjoys teasing others. Although his attitude makes him hard to get along with, JJ is a decent person and works hard to the best skater in the world. Aside from skating, JJ plays in a band and enjoys doing volunteer work during the off-season.
Phichit Chulanont (พิชิต จุลานนท์, ピチット・チュラノン, Pichitto Churanon)
Voiced by: Kenshō Ono[16][17] (Japanese); Joel McDonald (English)[3])
A 20-year-old figure skater from Thailand, Phichit was Yuri K.'s rink mate and college roommate during their time in Detroit, thus becoming good friends and even taught each other some words in their native languages. After Yuri K. left Detroit, Phichit returns to Thailand and is currently trains at the Imperial World ice rink in his hometown, Bangkok. Phichit is kind, cheerful, and loves to go on SNS and takes selfies, a passion he shares with his friends, Guang-Hong and Leo. According to Celestino, Phichit possess natural flair in his skating and has strong wish to represent his country with his figure skating career. He is shown to have owned multiple hamsters in Detroit during a flashback.
Michele Crispino (ミケーレ・クリスピーノ, Mikēre Kurisupīno)
Voiced by: Tomoaki Maeno[16][17] (Japanese); J. Michael Tatum[21] (English)
A 22-year-old figure skater from Italy, Michele, nicknamed "Mickey", is a serious and uptight person with a short-temper, often snapping at his fellow skater, Emil Nekola. He is very close to his twin sister, Sara, but is also overly dependent of her presence when he skates and becomes overprotective when other men approach her. Eventually, Sara makes Michele realize that his dependence on her isn't healthy and that he no longer needs her around to skate well.
Kenjirō Minami (南 健次郎, Minami Kenjiro)
Voiced by: Ayumu Murase[16][17] (Japanese); Trina Nishimura[3] (English)
A 17-year-old figure skater from Japan, Minami is a bright and innocent skater who is just making his senior debut. He is a huge fan of Yuri K., whom he greatly idolizes. He lives in Fukuoka, and his parents are doctors and he has an older brother currently studying at a medical college. During the season Yuri K. lost his first Grand Prix Final, Minami had beaten Yuri K. once at Nationals and is considered the number one star among young skaters. Despite this, Minami is humble and is encouraging to his fellow skaters, and still greatly admires Yuri K. as his role model and rival. Minami currently trains at the Hakata Skate Club with his coach, Kanako.
Seung Gil Lee (이승길, イ・スンギル, I Sungiru)[4]
Voiced by: Kenji Nojima[16][17] (Japanese); Oscar Seung[21] (English)
A 20-year-old figure skater from South Korea, Seung Gil is a stoic and calculating young man. He is generally emotionless, even during his programs, and only thinks about ice skating and improving his skills. Due to his handsome looks, he's very popular, especially with women, but pays no attention to his fans and often treats other people in a cold, detached manner.
Emil Nekola (エミル・ネコラ, Emiru Nekora)
Voiced by: Satoshi Hino[22] (Japanese); Justin Briner[23] (English)
An 18-year-old figure skater from the Czech Republic, Emil has a pure heart and mannerly disposition, which makes it easy for Michele to boss him around. He has a dynamic skating style and likes playing extreme sports during the off-season, which makes his coach anxious.
Georgi Popovich (ギオルギー・ポポーヴィッチ, Giorugī Popōvitchi)
Voiced by: Wataru Hatano[22] (Japanese); David Trosko[3] (English)
A 27-year-old figure skater from Russia, Georgi is Yuri P.'s, Mila's, and formerly Victor's rink mate and trains with them under their coach, Yakov. He is a diligent and effective student with great confidence in his artistic sensitivity. With Victor taking a season off to coach Yuri K., Georgi is now considered the best Russian male skater. He used to have an ice dancer girlfriend named Anya, but was recently dumped by her and used his heartbreak to fuel his Grand Prix programs.
Some confusion exists around Georgi's age. When the character is first introduced in episode 6, the Japanese text states his age as 25. The English translation, however, is given as 27. Later, in the Japanese version of the same episode, the announcer states that he is 27 years old, but the English dubbed version translates it as 25. The official Japanese Yuri on Ice website lists his age as 27 as do the English subtitled translations.
Leo de la Iglesia (レオ・デ・ラ・イグレシア, Reo de ra Igureshia)
Voiced by: Shunichi Toki[22] (Japanese); Ricco Fajardo[3] (English)
A 19-year-old figure skater from the United States, Leo is a Mexican-American who creates his own choreography, causing his programs to be full of originality that entertains the audience. He is an energetic and considerate individual with a great love for music. Like his friends Phichit and Guang-Hong, he enjoys frequenting and updating SNS.
Mila Babicheva (ミラ・バビチェヴァ, Mira Babicheva)
Voiced by: Mari Doi (Japanese); Monica Rial (English)[3]
An 18-year-old figure skater from Russia, Mila is Yuri P.'s and Georgi's rink mate and trains with them under their coach, Yakov. She's friendly and likes to tease Yuri P., much to his annoyance. She is ranked third as one of the world's top female ice skaters.
Sara Crispino (サーラ・クリスピーノ, Sāra Kurisupīno)
Voiced by: Marika Minase (Japanese); Tia Ballard[21] (English)
A 22-year-old figure skater from Italy, Sara is Michele's twin sister and the fourth best female skater in the world. Unlike her short-tempered brother, Sara is friendly and more mature, recognizing Michele's attachment to her is unhealthy and will affect both of their careers unless they're able to skate without the other being around. Eventually, Sara makes Michele realize that his dependence on her isn't healthy and that they no longer need each other around when they skate.
Cameo appearances[edit]
Nobunari Oda (織田 信成, Oda Nobunari)
Voiced by: himself
A 29-year-old retired Japanese champion figure skater, Oda joins Morooka as a guest commentator for the Grand Prix Final short program.
Stéphane Lambiel (ステファン・ランビエール, Sutefan Ranbiēru)
Voiced by: himself (Japanese); Josh Grelle (English)
A 31-year-old retired Swiss champion figure skater, Lambiel is a guest commentator for the French coverage of the Grand Prix Final free skate.
References[edit]
^ Jump up to: a b c "Yuri on Ice TV Anime's 1st Teaser Reveals Staff, Cast". Anime News Network. March 27, 2016. Retrieved April 1, 2016.
^ Jump up to: a b c "TV Anime "Yuri!!! on ICE" will start in October 2016 on TV Asahi and other broadcast stations!!! Character setting image and profile are open to public!!!". ©Hasetsu Townspeople／Yuri!!! on ICE PROJECT. July 29, 2016. Retrieved July 29, 2016.
^ Jump up to: a b c d e f g h i j k l m n o p "Fall 2016 SimulDub™ English Cast Announcements – For The Love of Dubs". Retrieved October 24, 2016.
^ Jump up to: a b c d e 「ユーリ!!! on ICE」公式サイト CHARACTER (in Japanese). Japan: はせつ町民会／ユーリ!!! on ICE 製作委員会. Retrieved 19 January 2017.
Jump up ^ @soukatsu_ (22 March 2017). "fun little tidbit from the YOI..." (Tweet) – via Twitter.
Jump up ^ https://wavemotioncannon.com/2016/12/20/miracle-on-ice-how-mitsuro-kubo-created-her-charactersanimage-january-2017/
Jump up ^ @soukatsu_ (22 March 2017). "fun little tidbit from the YOI..." (Tweet) – via Twitter.
^ Jump up to: a b c d e f "Sonny Strait's Facebook". Facebook.
Jump up ^ "Yuri!!! on Ice TV Anime Casts Jun Fukuyama". Anime News Network. Retrieved August 8, 2016.
Jump up ^ "Japanese Cast List". December 1, 2016.
Jump up ^ "Christophe's Coach Japanese VA". December 27, 2016.
^ Jump up to: a b "JJ's Parents". December 27, 2016.
Jump up ^ "Isabella's Japanese VA". December 27, 2016.
Jump up ^ https://twitter.com/DawnMBennettVA/status/813907336062533632
Jump up ^ "Seung Gil's Coach Japanese VA". December 27, 2016.
^ Jump up to: a b c d e f g h "Additional cast members announced". ©Hasetsu Townspeople／Yuri!!! on ICE PROJECT. August 24, 2016. Retrieved August 24, 2016.
^ Jump up to: a b c d e f g h "Yuri!!! on Ice Anime's New Promo Video Reveals Casting of Mamoru Miyano, Tomoaki Maeno, More". Anime News Network. August 21, 2016. Retrieved August 21, 2016.
Jump up ^ @sonnystrait (December 30, 2016). "@tsukkihabs @MikeMcFarlandVA Very..." (Tweet) – via Twitter.
Jump up ^ @TheHowardWang (December 1, 2016). "I voice Guang-hong Ji in the Simuldub..." (Tweet) – via Twitter.
Jump up ^ @InmanJeremy (December 14, 2016). "To all the haters that said I'd never..." (Tweet) – via Twitter.
^ Jump up to: a b c @sonnystrait (December 30, 2016). "@queenira2 justin Briner Ian Sinclair..." (Tweet) – via Twitter.
^ Jump up to: a b c "Yuri!!! on Ice Anime Reveals Ending Theme, Premiere Date, Additional Cast". Anime News Network. September 5, 2016. Retrieved September 5, 2016.
Jump up ^ @justinbriner (December 30, 2016). "Privileged to have joined a few more..." (Tweet) – via Twitter.
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