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Sentence clause structure
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In grammar, sentence clause structure is the classification of sentences based on the number and kind of clauses in their syntactic structure. Such division is an element of traditional grammar.
Contents
[hide]
1 Types
2 Simple sentences
3 Compound sentences
4 Complex and compound-complex sentences
5 Incomplete sentence
6 Run-on (fused) sentences
7 See also
8 References
9 External links
Types[edit]
A simple sentence consists of only one clause. A compound sentence consists of two or more independent clauses. A complex sentence has at least one independent clause plus at least one dependent clause.[1] A set of words with no independent clause may be an incomplete sentence, also called a sentence fragment.
A sentence consisting of at least one dependent clauses and at least two independent clauses may be called a complex-compound sentence or compound-complex sentence.
Sentence 1 is an example of a simple sentence. Sentence 2 is compound because "so" is considered a coordinating conjunction in English, and sentence 3 is complex. Sentence 4 is compound-complex (also known as complex-compound). Example 5 is a sentence fragment.
I like pumpkin pie.
I don't know how to bake, so I buy my bread.
I enjoyed the apple pie that you bought for me.
The dog lived in the garden, but the cat, who was smarter, lived inside the house.
What an idiot.
The simple sentence in example 1 contains one clause. Example 2 has two clauses (I don't know how to bake and I buy my bread), combined into a single sentence with the coordinating conjunction so. In example 3, I enjoyed the apple pie is an independent clause, and that you bought for me is a dependent clause; the sentence is thus complex. In sentence 4, The dog lived in the garden and the cat lived inside the house are both independent clauses; who was smarter is a dependent clause. Example 5 features a noun phrase but no verb. It is not a grammatically complete clause.
Simple sentences[edit]
A simple sentence structure contains one independent clause and no dependent clauses.[2]
I run.
This simple sentence has one independent clause which contains one subject, I, and one predicate, run.
The girl ran into her bedroom.
This simple sentence has one independent clause which contains one subject, girl, and one predicate, ran into her bedroom. The predicate is a verb phrase that consists of more than one word.
In the backyard, the dog barked and howled at the cat.
This simple sentence has one independent clause which contains one subject, dog, and one predicate, barked and howled at the cat. This predicate has two verbs, known as a compound predicate: barked and howled. This compound verb should not be confused with a compound sentence. In the backyard and at the cat are prepositional phrases.
Compound sentences[edit]
A compound sentence is composed of at least two independent clauses. It does not require a dependent clause. The clauses are joined by a coordinating conjunction (with or without a comma), a semicolon that functions as a conjunction, a colon instead of a semicolon between two sentences when the second sentence explains or illustrates the first sentence and no coordinating conjunction is being used to connect the sentences, or a conjunctive adverb preceded by a semicolon. A conjunction can be used to make a compound sentence. Conjunctions are words such as for, and, nor, but, or, yet, and so. Examples: He is going fishing on Friday is an independent clause. "He" is the subject, "is going" is the action, and a complete thought is expressed.
The coordinator "and" is used, and a comma is correctly used before the "and."http://www.yourdictionary.com/
I started on time, but I arrived late.
I will accept your offer or decline it; these are the two options.
The law was passed: from April 1, all cars would have to be tested.
The war was lost; consequently, the whole country was occupied.
The use of a comma to separate two independent clauses without the addition of an appropriate conjunction is called a comma splice and is generally considered an error (when used in the English language).[2] Example:
The sun was shining, everyone appeared happy.
Complex and compound-complex sentences[edit]
Main article: Dependent clause
A complex sentence has one or more dependent clauses (also called subordinate clauses). Since a dependent clause cannot stand on its own as a sentence, complex sentences must also have at least one independent clause. In short, a sentence with one or more dependent clauses and at least one independent clause is a complex sentence. A sentence with two or more independent clauses plus one or more dependent clauses is called compound-complex or complex-compound.
In addition to a subject and a verb, dependent clauses contain a subordinating conjunction or similar word. There are a large number of subordinating conjunctions in English. Some of these give the clause an adverbial function, specifying time, place, or manner. Such clauses are called adverbial clauses.
When I stepped out into the bright sunlight, from the darkness of the movie house, I had only two things on my mind. (S. E. Hinton, The Outsiders)
This complex sentence contains an adverbial clause, When I stepped out into the bright sunlight from the darkness of the movie house. The adverbial clause describes when the action of the main clause, I had only two things on my mind, took place.
A relative clause is a dependent clause that modifies a noun or noun phrase in the independent clause. In other words, the relative clause functions similar to an adjective.
Let him who has been deceived complain. (Miguel de Cervantes, Don Quixote)
You, who have never known your family, see them standing around you. (J.K. Rowling, Harry Potter and the Sorcerer's Stone)
In the first example, the restrictive relative clause who has been deceived specifies or defines the meaning of him in the independent clause, Let him complain. In the second example, the non-restrictive relative clause who have never known your family describes you in the independent clause, You see them standing around you.
A noun clause is a dependent clause that functions like a noun. A noun clause may function as the subject of a clause, or as a predicate nominative or an object.
What she had realised was that love was that moment when your heart was about to burst. (Stieg Larsson, The Girl with the Dragon Tattoo)
In this sentence the independent clause contains two noun clauses. The noun clause What she had realized serves as the subject of the verb was, and that love was that moment serves as complement. The sentence also contains an adverbial clause, when your heart was about to burst.
Incomplete sentence[edit]
An incomplete sentence, or sentence fragment, is a set of words which does not form a complete sentence, either because it does not express a complete thought[1] or because it lacks some grammatical element, such as a subject or a verb.[3] A dependent clause without an independent clause is one example of an incomplete sentence.
Some prescriptive grammars[4] consider sentences starting with a conjunction such as but or and to be incomplete sentences, but this style prescription has "no historical or grammatical foundation".[5] Computer grammar checkers often highlight incomplete sentences. If the context is clear from the rest of the paragraph, however, an incomplete sentence may be considered perfectly acceptable English.[6][unreliable source?]
Run-on (fused) sentences[edit]
A run-on or fused sentence consists of two or more independent clauses (i.e., clauses with subject and predicate) that are joined without any appropriate punctuation. An example of such a grammatically incorrect sentence could be, "It is nearly half past five we cannot reach town before dark." Some common remedies would be to separate the two independent clauses (between "five" and "we") with either a period [...five. We...], a comma and conjunction (...five, and we...), or a semicolon (...five; we...). Joining the two independent clauses with only a comma is actually another common grammatical error, the comma splice. James Joyce's novel Ulysses employs streams of consciousness, which takes literary license by intentionally breaking this grammatical rule by use of long, punctuation-free, run-on sentences, particularly in the final chapter "Penelope".
In general, run-on sentences occur when two or more independent clauses are joined without using a coordinating conjunction (i.e. for, and, nor, but, or, yet, so) or correct punctuation (i.e. semicolon, dash, or period),.[7][8][9] A run-on sentence can be as short as four words, for instance: I drive she walks., or even I drive, she walks, because in this case, there are two subjects paired with two intransitive verbs. An imperative sentence like "Run walk" can be a run-on even if it only has two words.
While some sources view comma splices as a form of run-on sentence,[8] others limit the term to independent clauses that are joined without punctuation.[7][10]
See also[edit]
Linguistics portal
Comma splice
References[edit]
^ Jump up to: a b Huddleston, Rodney (1984). Introduction to the Grammar of English. Cambridge University Press. ISBN 978-0-521-29704-2.
^ Jump up to: a b Rozakis, Laurie (2003). The Complete Idiot's Guide to Grammar and Style. Alpha. pp. 167–168. ISBN 1-59257-115-8.
Jump up ^ Sinclair, Christine (2007). Grammar: Getting it Right. McGraw-Hill. ISBN 978-0-335-22008-3.
Jump up ^ e.g. H. W. Fowler in Modern English Usage on BUT, p. 60 in the first edition.
Jump up ^ The Chicago Manual of Style (16th ed.). Chicago: University of Chicago Press. 2010. p. 257. ISBN 978-0-226-10420-1.
Jump up ^ "Sentence fragments". commnet.edu. 20 December 2015. Retrieved 22 March 2014.
^ Jump up to: a b Berry, Chris; Brizee, Allen (2006-08-31). "Runons – Comma Splices Fused Sentences". Retrieved 2008-01-24.
^ Jump up to: a b "Run-on Sentences, Comma Splices". Retrieved 2008-01-24.
Jump up ^ "English Grammar Lesson - Run-Ons! - ELC". ELC - English Language Center. 2017-05-19. Retrieved 2017-09-14.
Jump up ^ Hairston, Maxine; Ruszkiewicz, John J; Friend, Christy (1998). "The Scott Foresman Handbook for Writers" (5th ed.). New York: Longman: 509.
External links[edit]
Look up incomplete sentence in Wiktionary, the free dictionary.
"Sentence Types". Online Writing Lab. Purdue University. 1995. Retrieved 2 February 2014.
Campos Noguera, José Manuel. "Complex sentences". English Post: English Language Learning and Teaching. Retrieved 2 February 2014.
"Independent and Dependent Clauses" (PDF).
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Correlation does not imply causation
From Wikipedia, the free encyclopedia
Jump to: navigation, search
See also: Illusory correlation
In statistics, many statistical tests calculate correlations between variables and when two variables are found to be correlated, it is tempting to assume that this shows that one variable causes the other.[1][2] That "correlation proves causation," is considered a questionable cause logical fallacy when two events occurring together are taken to have established a cause-and-effect relationship. This fallacy is also known as cum hoc ergo propter hoc, Latin for "with this, therefore because of this," and "false cause." A similar fallacy, that an event that followed another was necessarily a consequence of the first event, is the post hoc ergo propter hoc (Latin for "after this, therefore because of this.") fallacy.
For example, in a widely studied case, numerous epidemiological studies showed that women taking combined hormone replacement therapy (HRT) also had a lower-than-average incidence of coronary heart disease (CHD), leading doctors to propose that HRT was protective against CHD. But randomized controlled trials showed that HRT caused a small but statistically significant increase in risk of CHD. Re-analysis of the data from the epidemiological studies showed that women undertaking HRT were more likely to be from higher socio-economic groups (ABC1), with better-than-average diet and exercise regimens. The use of HRT and decreased incidence of coronary heart disease were coincident effects of a common cause (i.e. the benefits associated with a higher socioeconomic status), rather than a direct cause and effect, as had been supposed.[3]
As with any logical fallacy, identifying that the reasoning behind an argument is flawed does not imply that the resulting conclusion is false. In the instance above, if the trials had found that hormone replacement therapy does in fact have a negative incidence on the likelihood of coronary heart disease the assumption of causality would have been correct, although the logic behind the assumption would still have been flawed. Indeed, a few go further, using correlation as a basis for testing a hypothesis to try to establish a true causal relationship; examples are the Granger causality test and convergent cross mapping.[clarification needed]
Contents
[hide]
1 Usage
2 General pattern
3 Examples of illogically inferring causation from correlation
3.1 B causes A (reverse causation or reverse causality)
3.2 Third factor C (the common-causal variable) causes both A and B
3.3 Bidirectional causation: A causes B, and B causes A
3.4 The relationship between A and B is coincidental
4 Determining causation
4.1 In academia
4.2 Causality construed from counterfactual states
4.3 Causality predicted by an extrapolation of trends
5 Use of correlation as scientific evidence
6 See also
7 References
7.1 Bibliography
8 External links
Usage[edit]
In logic, the technical use of the word "implies" means "is a sufficient circumstance for".[citation needed] This is the meaning intended by statisticians when they say causation is not certain. Indeed, p implies q has the technical meaning of the material conditional: if p then q symbolized as p → q. That is "if circumstance p is true, then q follows." In this sense, it is always correct to say "Correlation does not imply causation."
However, in casual use, the word "implies" loosely means suggests rather than requires. The idea that correlation and causation are connected is certainly true; where there is causation, there is a likely correlation. Indeed, correlation is used when inferring causation; the important point is that such inferences are made after correlations are confirmed as real and all causational relationship are systematically explored using large enough data sets.
General pattern[edit]
For any two correlated events, A and B, the different possible relationships include[citation needed]:
A causes B (direct causation);
B causes A (reverse causation);
A and B are consequences of a common cause, but do not cause each other;
A and B both cause C, which is (explicitly or implicitly) conditioned on. If A and B cause C, why do A and B have to be correlated?;
A causes B and B causes A (bidirectional or cyclic causation);
A causes C which causes B (indirect causation);
There is no connection between A and B; the correlation is a coincidence.
Thus there can be no conclusion made regarding the existence or the direction of a cause-and-effect relationship only from the fact that A and B are correlated. Determining whether there is an actual cause-and-effect relationship requires further investigation, even when the relationship between A and B is statistically significant, a large effect size is observed, or a large part of the variance is explained.
Examples of illogically inferring causation from correlation[edit]
B causes A (reverse causation or reverse causality)[edit]
Reverse causation or reverse causality or wrong direction is an informal fallacy of questionable cause where cause and effect are reversed. The cause is said to be the effect and vice versa.
Example 1
The faster windmills are observed to rotate, the more wind is observed to be.
Therefore wind is caused by the rotation of windmills. (Or, simply put: windmills, as their name indicates, are machines used to produce wind.)
In this example, the correlation (simultaneity) between windmill activity and wind velocity does not imply that wind is caused by windmills. It is rather the other way around, as suggested by the fact that wind doesn’t need windmills to exist, while windmills need wind to rotate. Wind can be observed in places where there are no windmills or non-rotating windmills—and there are good reasons to believe that wind existed before the invention of windmills.
Example 2
When a country's debt rises above 90% of GDP, growth slows.
Therefore, high debt causes slow growth.
This argument by Carmen Reinhart and Kenneth Rogoff was refuted by Paul Krugman on the basis that they got the causality backwards: in actuality, slow growth causes debt to increase.[4]
Example 3
Driving a wheelchair is dangerous, because most people who drive them have had an accident.
Example 4
In other cases it may simply be unclear which is the cause and which is the effect. For example:
Children that watch a lot of TV are the most violent. Clearly, TV makes children more violent.
This could easily be the other way round; that is, violent children like watching more TV than less violent ones.
Example 5
A correlation between recreational drug use and psychiatric disorders might be either way around: perhaps the drugs cause the disorders, or perhaps people use drugs to self medicate for preexisting conditions. Gateway drug theory may argue that marijuana usage leads to usage of harder drugs, but hard drug usage may lead to marijuana usage (see also confusion of the inverse). Indeed, in the social sciences where controlled experiments often cannot be used to discern the direction of causation, this fallacy can fuel long-standing scientific arguments. One such example can be found in education economics, between the screening/signaling and human capital models: it could either be that having innate ability enables one to complete an education, or that completing an education builds one's ability.
Example 6
A historical example of this is that Europeans in the Middle Ages believed that lice were beneficial to your health, since there would rarely be any lice on sick people. The reasoning was that the people got sick because the lice left. The real reason however is that lice are extremely sensitive to body temperature. A small increase of body temperature, such as in a fever, will make the lice look for another host. The medical thermometer had not yet been invented, so this increase in temperature was rarely noticed. Noticeable symptoms came later, giving the impression that the lice left before the person got sick.[citation needed]
In other cases, two phenomena can each be a partial cause of the other; consider poverty and lack of education, or procrastination and poor self-esteem. One making an argument based on these two phenomena must however be careful to avoid the fallacy of circular cause and consequence. Poverty is a cause of lack of education, but it is not the sole cause, and vice versa.
Third factor C (the common-causal variable) causes both A and B[edit]
Main article: Spurious relationship
The third-cause fallacy (also known as ignoring a common cause[5] or questionable cause[5]) is a logical fallacy where a spurious relationship is confused for causation. It asserts that X causes Y when, in reality, X and Y are both caused by Z. It is a variation on the post hoc ergo propter hoc fallacy and a member of the questionable cause group of fallacies.
All of these examples deal with a lurking variable, which is simply a hidden third variable that affects both causes of the correlation. A difficulty often also arises where the third factor, though fundamentally different from A and B, is so closely related to A and/or B as to be confused with them or very difficult to scientifically disentangle from them (see Example 4).
Example 1
Sleeping with one's shoes on is strongly correlated with waking up with a headache.
Therefore, sleeping with one's shoes on causes headache.
The above example commits the correlation-implies-causation fallacy, as it prematurely concludes that sleeping with one's shoes on causes headache. A more plausible explanation is that both are caused by a third factor, in this case going to bed drunk, which thereby gives rise to a correlation. So the conclusion is false.
Example 2
Young children who sleep with the light on are much more likely to develop myopia in later life.
Therefore, sleeping with the light on causes myopia.
This is a scientific example that resulted from a study at the University of Pennsylvania Medical Center. Published in the May 13, 1999 issue of Nature,[6] the study received much coverage at the time in the popular press.[7] However, a later study at Ohio State University did not find that infants sleeping with the light on caused the development of myopia. It did find a strong link between parental myopia and the development of child myopia, also noting that myopic parents were more likely to leave a light on in their children's bedroom.[8][9][10][11] In this case, the cause of both conditions is parental myopia, and the above-stated conclusion is false.
Example 3
As ice cream sales increase, the rate of drowning deaths increases sharply.
Therefore, ice cream consumption causes drowning.
This example fails to recognize the importance of time of year and temperature to ice cream sales. Ice cream is sold during the hot summer months at a much greater rate than during colder times, and it is during these hot summer months that people are more likely to engage in activities involving water, such as swimming. The increased drowning deaths are simply caused by more exposure to water-based activities, not ice cream. The stated conclusion is false.
Example 4
A hypothetical study shows a relationship between test anxiety scores and shyness scores, with a statistical r value (strength of correlation) of +.59.[12]
Therefore, it may be simply concluded that shyness, in some part, causally influences test anxiety.
However, as encountered in many psychological studies, another variable, a "self-consciousness score", is discovered that has a sharper correlation (+.73) with shyness. This suggests a possible "third variable" problem, however, when three such closely related measures are found, it further suggests that each may have bidirectional tendencies (see "bidirectional variable", above), being a cluster of correlated values each influencing one another to some extent. Therefore, the simple conclusion above may be false.
Example 5
Since the 1950s, both the atmospheric CO2 level and obesity levels have increased sharply.
Hence, atmospheric CO2 causes obesity.
Richer populations tend to eat more food and produce more CO2.
Example 6
HDL ("good") cholesterol is negatively correlated with incidence of heart attack.
Therefore, taking medication to raise HDL decreases the chance of having a heart attack.
Further research[13] has called this conclusion into question. Instead, it may be that other underlying factors, like genes, diet and exercise, affect both HDL levels and the likelihood of having a heart attack; it is possible that medicines may affect the directly measurable factor, HDL levels, without affecting the chance of heart attack.
Bidirectional causation: A causes B, and B causes A[edit]
Causality is not necessarily one-way; in a predator-prey relationship, predator numbers affect prey numbers, but prey numbers, i.e. food supply, also affect predator numbers.
The relationship between A and B is coincidental[edit]
The two variables aren't related at all, but correlate by chance. The more things are examined, the more likely it is that two unrelated variables will appear to be related. For example:
The result of the last home game by the Washington Redskins prior to the presidential election predicted the outcome of every presidential election from 1936 to 2000 inclusive, despite the fact that the outcomes of football games had nothing to do with the outcome of the popular election. This streak was finally broken in 2004 (or 2012 using an alternative formulation of the original rule).
A collection of such coincidences[14] finds that for example, there is a 99.79% correlation for the period 1999-2009 between U.S. spending on science, space, and technology; and the number of suicides by suffocation, strangulation, and hanging.
The Mierscheid law, which correlates the Social Democratic Party of Germany's share of the popular vote with the size of crude steel production in Western Germany.
Alternating bald–hairy Russian leaders: A bald (or obviously balding) state leader of Russia has succeeded a non-bald ("hairy") one, and vice versa, for nearly 200 years.
Determining causation[edit]
In academia[edit]
Main articles: Causality and Causality (physics)
The nature of causality is systematically investigated in several academic disciplines, including philosophy and physics.
In academia, there are a significant number of theories on causality; The Oxford Handbook of Causation (Beebee, Hitchcock & Menzies 2009) encompasses 770 pages. Among the more influential theories within philosophy are Aristotle's Four causes and Al-Ghazali's occasionalism.[15] David Hume argued that beliefs about causality are based on experience, and experience similarly based on the assumption that the future models the past, which in turn can only be based on experience – leading to circular logic. In conclusion, he asserted that causality is not based on actual reasoning: only correlation can actually be perceived.[16] Immanuel Kant, according to Beebee, Hitchcock & Menzies (2009), held that "a causal principle according to which every event has a cause, or follows according to a causal law, cannot be established through induction as a purely empirical claim, since it would then lack strict universality, or necessity".
Outside the field of philosophy, theories of causation can be identified in classical mechanics, statistical mechanics, quantum mechanics, spacetime theories, biology, social sciences, and law.[15] To establish a correlation as causal within physics, it is normally understood that the cause and the effect must connect through a local mechanism (cf. for instance the concept of impact) or a nonlocal mechanism (cf. the concept of field), in accordance with known laws of nature.
From the point of view of thermodynamics, universal properties of causes as compared to effects have been identified through the Second law of thermodynamics, confirming the ancient, medieval and Cartesian[17] view that "the cause is greater than the effect" for the particular case of thermodynamic free energy. This, in turn, is challenged[dubious – discuss] by popular interpretations of the concepts of nonlinear systems and the butterfly effect, in which small events cause large effects due to, respectively, unpredictability and an unlikely triggering of large amounts of potential energy.
Causality construed from counterfactual states[edit]
See also: Verificationism
Intuitively, causation seems to require not just a correlation, but a counterfactual dependence. Suppose that a student performed poorly on a test and guesses that the cause was his not studying. To prove this, one thinks of the counterfactual – the same student writing the same test under the same circumstances but having studied the night before. If one could rewind history, and change only one small thing (making the student study for the exam), then causation could be observed (by comparing version 1 to version 2). Because one cannot rewind history and replay events after making small controlled changes, causation can only be inferred, never exactly known. This is referred to as the Fundamental Problem of Causal Inference – it is impossible to directly observe causal effects.[18]
A major goal of scientific experiments and statistical methods is to approximate as best possible the counterfactual state of the world.[19] For example, one could run an experiment on identical twins who were known to consistently get the same grades on their tests. One twin is sent to study for six hours while the other is sent to the amusement park. If their test scores suddenly diverged by a large degree, this would be strong evidence that studying (or going to the amusement park) had a causal effect on test scores. In this case, correlation between studying and test scores would almost certainly imply causation.
Well-designed experimental studies replace equality of individuals as in the previous example by equality of groups. The objective is to construct two groups that are similar except for the treatment that the groups receive. This is achieved by selecting subjects from a single population and randomly assigning them to two or more groups. The likelihood of the groups behaving similarly to one another (on average) rises with the number of subjects in each group. If the groups are essentially equivalent except for the treatment they receive, and a difference in the outcome for the groups is observed, then this constitutes evidence that the treatment is responsible for the outcome, or in other words the treatment causes the observed effect. However, an observed effect could also be caused "by chance", for example as a result of random perturbations in the population. Statistical tests exist to quantify the likelihood of erroneously concluding that an observed difference exists when in fact it does not (for example see P-value).
Causality predicted by an extrapolation of trends[edit]
See also: Inertia
See also: Life-time of correlation
When experimental studies are impossible and only pre-existing data are available, as is usually the case for example in economics, regression analysis can be used. Factors other than the potential causative variable of interest are controlled for by including them as regressors in addition to the regressor representing the variable of interest. False inferences of causation due to reverse causation (or wrong estimates of the magnitude of causation due the presence of bidirectional causation) can be avoided by using explanators (regressors) that are necessarily exogenous, such as physical explanators like rainfall amount (as a determinant of, say, futures prices), lagged variables whose values were determined before the dependent variable's value was determined, instrumental variables for the explanators (chosen based on their known exogeneity), etc. See Causality#Statistics and economics. Spurious correlation due to mutual influence from a third, common, causative variable, is harder to avoid: the model must be specified such that there is a theoretical reason to believe that no such underlying causative variable has been omitted from the model.
Use of correlation as scientific evidence[edit]
This section's factual accuracy is disputed. Relevant discussion may be found on Talk:Correlation does not imply causation. Please help to ensure that disputed statements are reliably sourced. (July 2015) (Learn how and when to remove this template message)
Much of scientific evidence is based upon a correlation of variables[20] – they are observed to occur together. Scientists are careful to point out that correlation does not necessarily mean causation. The assumption that A causes B simply because A correlates with B is often not accepted as a legitimate form of argument.
However, sometimes people commit the opposite fallacy – dismissing correlation entirely. This would dismiss a large swath of important scientific evidence.[20] Since it may be difficult or ethically impossible to run controlled double-blind studies, correlational evidence from several different angles may be useful for prediction despite failing to provide evidence for causation. For example, social workers might be interested in knowing how child abuse relates to academic performance. Although it would be unethical to perform an experiment in which children are randomly assigned to receive or not receive abuse, researchers can look at existing groups using a non-experimental correlational design. If in fact a negative correlation exists between abuse and academic performance, researchers could potentially use this knowledge of a statistical correlation to make predictions about children outside the study who experience abuse, even though the study failed to provide causal evidence that abuse decreases academic performance. [21] The combination of limited available methodologies with the dismissing correlation fallacy has on occasion been used to counter a scientific finding. For example, the tobacco industry has historically relied on a dismissal of correlational evidence to reject a link between tobacco and lung cancer,[22] as did biologist and statistician Ronald Fisher.[23][24][25][26][27][28][29]
Correlation is a valuable type of scientific evidence in fields such as medicine, psychology, and sociology. But first correlations must be confirmed as real, and then every possible causative relationship must be systematically explored. In the end correlation alone cannot be used as evidence for a cause-and-effect relationship between a treatment and benefit, a risk factor and a disease, or a social or economic factor and various outcomes. It is one of the most abused types of evidence, because it is easy and even tempting to come to premature conclusions based upon the preliminary appearance of a correlation.[citation needed]
See also[edit]
Affirming the consequent
Alignments of random points
Apophenia:
Post-hoc analysis
Multiple comparisons problem
Look-elsewhere effect
Data dredging
Testing hypotheses suggested by the data
Bible code
Coincidence#Coincidence and causality
Confounding
Confusion of the inverse
French paradox
Design of experiments
Joint effect
Normally distributed and uncorrelated does not imply independent
Pirates and global warming
Reproducibility
Spurious relationship

References[edit]
Jump up ^ Tufte 2006, p. 5
Jump up ^ Aldrich, John (1995). "Correlations Genuine and Spurious in Pearson and Yule" (PDF). Statistical Science. 10 (4): 364–376. doi:10.1214/ss/1177009870. JSTOR 2246135. Archived from the original (PDF) on February 19, 2006.
Jump up ^ Lawlor DA, Davey Smith G, Ebrahim S (June 2004). "Commentary: the hormone replacement-coronary heart disease conundrum: is this the death of observational epidemiology?". Int J Epidemiol. 33 (3): 464–467. doi:10.1093/ije/dyh124. PMID 15166201. CS1 maint: Multiple names: authors list (link)
Jump up ^ "Reinhart-Rogoff, Continued".
^ Jump up to: a b Labossiere, M.C., Dr. LaBossiere's Philosophy Pages
Jump up ^ Quinn, Graham E.; Shin, Chai H.; Maguire, Maureen G.; Stone, Richard A. (May 1999). "Myopia and ambient lighting at night". Nature. 399 (6732): 113–114. doi:10.1038/20094. PMID 10335839.
Jump up ^ CNN, May 13, 1999. Night-light may lead to nearsightedness
Jump up ^ Ohio State University Research News, March 9, 2000. Night lights don't lead to nearsightedness, study suggests
Jump up ^ Zadnik, Karla; Jones, Lisa A.; Irvin, Brett C.; Kleinstein, Robert N.; Manny, Ruth E.; Shin, Julie A.; Mutti, Donald O. (2000). "Vision: Myopia and ambient night-time lighting". Nature. 404 (6774): 143–144. doi:10.1038/35004661. PMID 10724157.
Jump up ^ Gwiazda, J.; Ong, E.; Held, R.; Thorn, F. (2000). "Vision: Myopia and ambient night-time lighting". Nature. 404 (6774): 144–144. doi:10.1038/35004663. PMID 10724158.
Jump up ^ Stone, Richard A.; Maguire, Maureen G.; Quinn, Graham E. (2000). "Vision: reply: Myopia and ambient night-time lighting". Nature. 404 (6774): 144–144. doi:10.1038/35004665.
Jump up ^ Carducci, Bernardo J. (2009). The Psychology of Personality: Viewpoints, Research, and Applications (2nd ed.). John Wiley & Sons. ISBN 978-1-4051-3635-8.
Jump up ^ Ornish, Dean. "Cholesterol: The good, the bad, and the truth" [1] (retrieved 3 June 2011)
Jump up ^ "Spurious Correlations". Tylervigen.com. Retrieved 2017-10-08.
^ Jump up to: a b Beebee, Hitchcock & Menzies 2009
Jump up ^ Morris, William Edward (2001). "David Hume". The Stanford Encyclopedia of Philosophy.
Jump up ^ Lloyd, A.C. (1976). "The principle that the cause is greater than its effect". Phronesis. 21 (2): 146–156. doi:10.1163/156852876x00101. JSTOR 4181986.
Jump up ^ Holland, Paul W. (1986). "Statistics and Causal Inference". Journal of the American Statistical Association. 81 (396): 945–960. doi:10.1080/01621459.1986.10478354.
Jump up ^ Pearl, Judea (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press. ISBN 9780521773621.
^ Jump up to: a b Novella. "Evidence in Medicine: Correlation and Causation". Science and Medicine. Science-Based Medicine.
Jump up ^ Nielsen, Michael (2012-01-23). "If correlation doesn't imply causation, then what does? | DDI". Michaelnielsen.org. Retrieved 2017-10-08.
Jump up ^ "Evidence in Medicine: Correlation and Causation – Science-Based Medicine". Sciencebasedmedicine.org. 2009-11-18. Retrieved 2017-10-08.
Jump up ^ Silver, Nate (2015), The Signal and the Noise: Why So Many Predictions Fail – But Some Don't (2nd ed.), New York: Penguin Books, pp. 254–255
Jump up ^ Fisher, Ronald (July 6, 1957), "Dangers Of Cigarette-Smoking", The British Medical Journal, London: British Medical Association, 2: 43, doi:10.1136/bmj.2.5035.43, JSTOR 25383068 – via JSTOR, (Registration required (help))
Jump up ^ Fisher, Ronald (August 3, 1957), "Dangers Of Cigarette-Smoking", The British Medical Journal, London: British Medical Association, 2: 297–298, doi:10.1136/bmj.2.5039.297-b, JSTOR 25383439 – via JSTOR, (Registration required (help))
Jump up ^ Fisher, Ronald (1958), "Cigarettes, Cancer, and Statistics" (PDF), The Centennial Review of Arts & Science, East Lansing, Michigan: Michigan State University Press, 2: 151–166
Jump up ^ Fisher, Ronald (1958), "The Nature of Probability" (PDF), The Centennial Review of Arts & Science, East Lansing, Michigan: Michigan State University Press, 2: 261–274
Jump up ^ Fisher, Ronald (July 12, 1958), "Lung Cancer and Cigarettes" (PDF), Nature, London: Nature Publishing Group, 182: 108, doi:10.1038/182108a0
Jump up ^ Fisher, Ronald (August 30, 1958), "Cancer and Smoking" (PDF), Nature, London: Nature Publishing Group, 182: 596, doi:10.1038/182596a0
Bibliography[edit]
Beebee, Helen; Hitchcock, Christopher; Menzies, Peter (2009). The Oxford Handbook of Causation. Oxford University Press. ISBN 978-0-19-162946-4.
Tufte, Edward R. (2006). "The Cognitive Style of PowerPoint: Pitching Out Corrupts Within" (2nd ed.). Cheshire, Connecticut: Graphics Press. ISBN 0-9613921-5-0.
External links[edit]
"The Art and Science of cause and effect": a slide show and tutorial lecture by Judea Pearl
Causal inference in statistics: An overview, by Judea Pearl (September 2009)
Spurious Correlations, site searching and showing such correlations.
What Everyone Should Know about Statistical Correlation
[hide]
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text:
Ain't No Way to Treat a Lady
From Wikipedia, the free encyclopedia
Jump to: navigation, search
"Ain't No Way to Treat a Lady"
Single by Helen Reddy
from the album No Way to Treat a Lady
B-side
"Long Time Looking"
Released
August 1975
Format
7" single
Genre
Easy listening
Length
3:26
Label
Capitol
Songwriter(s)
Harriet Schock
Producer(s)
Joe Wissert
Helen Reddy singles chronology
"Bluebird"
(1975) "Ain't No Way to Treat a Lady"
(1975) "Somewhere in the Night"
(1976)
"Bluebird"
(1975) "Ain't No Way to Treat a Lady"
(1975) "Somewhere in the Night"
(1976)
"Ain't No Way to Treat a Lady" is the title of a 1975 top ten hit by Helen Reddy.
Contents
[hide]
1 Recording and release
2 Helen Reddy version
2.1 Schock's reaction
3 Chart performance
3.1 Weekly charts
3.2 Year-end charts
4 Other covers
5 Other songs
6 Later uses
7 See also
8 References
9 External links
Recording and release[edit]
An apparent farewell to a self-absorbed lover, "Ain't No Way to Treat a Lady" was first recorded by its composer Harriet Schock, who recalled having written its first verse and title on a serviette (napkin) on a plane while "leaving someone for...one of the last times I left him for the last time". The song was introduced on Schock's 1974 album Hollywood Town and issued as a single. Schock states that the program director at a key Los Angeles radio station was taken with the track but found its arrangement slow. 20th Century Records obligingly had Schock cut a brisker version which was delivered to the radio station on a Friday to start airing the following Monday. Schock recalls: "Well, Sunday the program director had a fight with someone at the station and quit." [attribution needed]
Helen Reddy version[edit]
Despite the failure of Schock's own single version of "Ain't No Way to Treat a Lady" – and also that of a concurrent cover by a group named LAX – the song gained a reasonably high-profile via its inclusion on the 1974 album One Hell of a Woman by Vikki Carr. In 1975, Helen Reddy recorded "Ain't No Way to Treat a Lady" for an album which was entitled No Way to Treat a Lady. Personnel on Reddy's album included guitarist Lee Ritenour.
Although the choice of lead single from No Way to Treat a Lady went to the Leon Russell song "Bluebird", the influential LA radio station 93 KHJ began playing "Ain't No Way to Treat a Lady" resulting in its release as a single in August 1975 with a chart peak that October of #8 on the Billboard Hot 100 (#5 on the Cash Box Top 100 Singles chart).
"Ain't No Way to Treat a Lady" reached #1 on the Billboard Easy Listening charts on October 4, 1975. Although Helen Reddy would have one further Easy Listening #1 – "I Can't Hear You No More" – for a total of eight, "Ain't No Way to Treat a Lady" would be Reddy's final single to go Top Ten Pop. "Ain't No Way to Treat a Lady" was also a hit (#12) in New Zealand and became Reddy's final chart item in her native Australia at #94.
Schock's reaction[edit]
Schock has suggested that while her intent in writing "Ain't No Way to Treat a Lady" was to reference a specific personal experience, the song has come to be seen as a statement of how women are generally treated by men "because it was a hit by [Helen Reddy], the same artist who spoke so widely for all women in 'I Am Woman' ... [and] apparently other women wanted to say [what Schock's song says] to someone. I received a number of calls from women telling me it was just the kick they needed to get that divorce."
Chart performance[edit]
Weekly charts[edit]
Chart (1975)
Peak
position
Australia[1] 94
Canada RPM Top Singles[2] 2
Canadian RPM Adult Contemporary[3] 2
New Zealand Singles Chart[4] 12
U.S. Billboard Hot 100[5] 8
U.S. Billboard Easy Listening[6] 1
U.S. Cash Box Top 100[7] 5
Year-end charts[edit]
Chart (1975)
Rank
Canada RPM Top Singles[8] 62
Canada RPM Adult Contemporary[9] 21
U.S. Billboard Hot 100[10] 89
U.S. Cash Box[11] 56
Other covers[edit]
A disco version of "Ain't No Way to Treat a Lady" was recorded by Gonzalez and issued as the followup to their 1979 hit "Haven't Stopped Dancin' Yet" but it was not a success. The song has also been recorded by Anita Sarawak and - in Swahili - by Letta Mbulu.[12] [13][14] A Finnish rendition of the song: "Sä Tunnet Mun", was recorded by Eija Merilä (fi) while the French rendition " Je Veux T'aimer Comme Une Femme" was recorded by Mireille Mathieu.
Other songs[edit]
A different song with the same title was written by Floyd Rose and performed by his band Q5 on their 1984 album Steel the Light. This song was covered by Great White twice. The first version is on their 1996 album Let It Rock, with lead singer Jack Russell singing in his normal range. The second version is included on the 2002 Great White tribute album, Recover, with Russell singing higher. This latter version is played and sung with a harder rock edge.
Later uses[edit]
In 2005, Raven Records Australia reissued the song as a two-fer with "One Way Ticket" included as a bonus track.
See also[edit]
List of number-one adult contemporary singles of 1975 (U.S.)
References[edit]
Jump up ^ "Australian Chart Book". Austchartbook.com.au. Archived from the original on 2016-03-05. Retrieved 2016-10-10.
Jump up ^ "Item Display - RPM - Library and Archives Canada". Collectionscanada.gc.ca. Retrieved 2016-10-10.
Jump up ^ "Item Display - RPM - Library and Archives Canada". Collectionscanada.gc.ca. Retrieved 2016-10-10.
Jump up ^ "NZ Top 40 Singles Chart | The Official New Zealand Music Chart". Nztop40.co.nz. 1975-11-10. Retrieved 2016-10-10.
Jump up ^ Joel Whitburn's Top Pop Singles 1955-1990 - ISBN 0-89820-089-X
Jump up ^ "Adult Contemporary Music Chart". Billboard. 1975-10-04. Retrieved 2016-10-10.
Jump up ^ "Archived copy". Archived from the original on 2015-12-23. Retrieved 2015-12-22.
Jump up ^ "Image : RPM Weekly - Library and Archives Canada". Bac-lac.gc.ca. Retrieved 2016-10-10.
Jump up ^ "Item Display - RPM - Library and Archives Canada". collectionscanada.gc.ca.
Jump up ^ "Top 100 Hits of 1975/Top 100 Songs of 1975". Musicoutfitters.com. Retrieved 2016-10-10.
Jump up ^ "Archived copy". Archived from the original on 2014-10-21. Retrieved 2014-10-21.
Jump up ^ Hyatt, Wesley (1999). The Billboard Book of Number One Adult Contemporary Hits. New York City: Watson-Guptill Publications. p. 161. ISBN 0-8230-7693-8.
Jump up ^ "Welcome to My Web Site!". Harriet Schock. 2014-04-30. Retrieved 2016-10-10.
Jump up ^ "AMG". Archived from the original on 22 January 2010. Retrieved 21 January 2010.
External links[edit]
Lyrics of this song at MetroLyrics
[hide]
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Helen Reddy
Studio albums
I Don't Know How to Love Him
Helen Reddy
I Am Woman
Long Hard Climb
Love Song for Jeffrey
Free and Easy
No Way to Treat a Lady
Music, Music
Ear Candy
We'll Sing in the Sunshine
Reddy
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Imagination
Feel So Young
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Live albums
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Compilations
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"One Way Ticket"
"I Don't Know How to Love Him"
"Crazy Love"
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"Leave Me Alone (Ruby Red Dress)"
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"You and Me Against the World"
"Angie Baby"
"Emotion"
"Ain't No Way to Treat a Lady"
"Somewhere in the Night"
"I Can't Hear You No More"
"You're My World"
"Candle on the Water"
"We'll Sing in the Sunshine"
Related articles
Discography
Toni Lamond
Tony Sheldon
"Voices That Care"
Book:Helen Reddy
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