A false correlation between two variables caused by a third variable?
singleAnswer singleAnswer
spurious correlation
spurious relationship spurious correlation
Correlation does not imply causation Spurious relationship
false correlation between two variables caused by a third variable A false correlation between two variables caused by a third variable?
Correlation does not imply causation Spurious relationship Statistics Illusory correlation Misuse of statistics Design of experiments Multiple comparisons problem Boolean satisfiability problem Multicollinearity False discovery rate
In statistics, the phrase &quot;correlation does not imply causation&quot; refers to the 
inability to legitimately deduce a <b>cause</b>-and-effect <b>relationship between two</b> 
<b>variables</b>&nbsp;... In statistics, a spurious relationship or spurious correlation is a mathematical 
relationship in which two or more events or variables are associated but not 
causally related, <b>due to</b> either coincidence or the presence of a certain <b>third</b>, 
unseen factor (referred to as a &quot;common response <b>variable</b>&quot;, ... Often one tests a 
null hypothesis of no <b>correlation between two variables</b>, and&nbsp;... Statistics is the discipline that concerns the collection, organization, analysis, 
interpretation and ... A hypothesis is proposed for the statistical <b>relationship</b> 
<b>between</b> the <b>two</b> data sets, ... <b>2</b> History; <b>3</b> Statistical data ... In both types of 
studies, the effect of differences of an independent <b>variable</b> (or <b>variables</b>) on the 
behavior of the&nbsp;... In psychology, illusory correlation is the phenomenon of perceiving a <b>relationship</b> 
<b>between variables</b> even when no such relationship exists. A <b>false</b> association&nbsp;... Statistics, when used in a misleading fashion, can trick the casual observer into 
believing ... Scientists have been known to fool themselves with statistics <b>due to</b> 
lack of knowledge ... <b>Multivariable</b> datasets have <b>two</b> or more features/
dimensions. ... of finding a <b>correlation between</b> any <b>two</b> sets of completely 
random <b>variables</b>. The design <b>of</b> experiments is the design <b>of</b> any task that aims <b>to</b> describe and 
explain the ... The experimental design may also identify control <b>variables</b> that 
must be held ... There are <b>multiple</b> approaches <b>for</b> determining the set <b>of</b> design 
points ... the conditions that <b>causes</b> the differences in outcomes, that is - a <b>third</b> 
<b>variable</b>. In statistics, the <b>multiple</b> comparisons, multiplicity or <b>multiple</b> testing problem 
occurs when one ... The interest in the problem <b>of multiple</b> comparisons began in 
the 1950s <b>with</b> ... In 1995, work on the <b>false</b> discovery rate began. ... random 
<b>variable</b>, and S, T, U, and V are unobservable random <b>variables</b>. ... 59 (<b>3</b>): 12:1–
12:22. In logic and computer science, the Boolean satisfiability problem is the problem 
<b>of</b> determining if there exists an interpretation that satisfies a given Boolean 
formula. In other words, it asks whether the <b>variables of</b> a given Boolean formula 
can ... The formula is satisfiable, by choosing x1 = <b>FALSE</b>, x<b>2</b> = <b>FALSE</b>, and x<b>3</b>&nbsp;... In statistics, multicollinearity (also collinearity) is a phenomenon in which one 
predictor <b>variable</b> in a <b>multiple</b> regression model can be linearly predicted from 
the others <b>with</b> a substantial degree <b>of</b> accuracy. In this situation the coefficient 
estimates <b>of</b> the <b>multiple</b> regression may ... Collinearity is a linear association 
<b>between two</b> explanatory <b>variables</b>. The <b>false</b> discovery rate (FDR) is a method <b>of</b> conceptualizing the rate <b>of</b> type I 
errors in null ... <b>3</b> Controlling procedures ... Suppose we have a number m <b>of</b> null 
hypotheses, denoted by: H1, H<b>2</b>, ..., Hm. ... observable random <b>variable</b>, and S, T, 
U, and V are unobservable random <b>variables</b>. ... <b>Correlation</b> &middot; Regression 
analysis&nbsp;...
Correlation does not imply causation Spurious relationship Statistics Multicollinearity Illusory correlation Misuse of statistics Endogeneity (econometrics) Design of experiments Confounding Causality
In statistics, the phrase &quot;<b>correlation</b> does not imply causation&quot; refers to the 
inability to legitimately deduce a <b>cause</b>-and-effect relationship <b>between two</b> 
<b>variables</b> ... <b>False</b> inferences of causation <b>due to</b> reverse causation (or wrong 
estimates of the ... <b>correlation due to</b> mutual influence from a <b>third</b>, common, 
causative <b>variable</b>,&nbsp;... In statistics, a spurious relationship or spurious correlation is a mathematical 
relationship in which two or more events or variables are associated but not 
causally related, <b>due to</b> either coincidence or the presence of a certain <b>third</b>, 
unseen factor (referred to as a &quot;common response <b>variable</b>&quot;, ... Often one tests a 
null hypothesis of no <b>correlation between two variables</b>, and&nbsp;... Statistics is the discipline that concerns the collection, organization, analysis, 
interpretation and ... A hypothesis is proposed for the statistical relationship 
<b>between</b> the <b>two</b> data sets, ... (or <b>variables</b>) on the behavior of the dependent 
<b>variable</b> are observed. ... The <b>correlation</b> phenomena could be <b>caused by a third</b>, 
previously&nbsp;... In statistics, multicollinearity (also collinearity) is a phenomenon in which one 
predictor <b>variable</b> in a <b>multiple</b> regression model can be linearly predicted from 
the others with a substantial degree of accuracy. In this situation the coefficient 
estimates of the <b>multiple</b> regression may ... Collinearity is a linear association 
<b>between two</b> explanatory <b>variables</b>. In psychology, illusory <b>correlation</b> is the phenomenon of perceiving a relationship 
<b>between variables</b> even when no such relationship exists. <b>A false</b> association&nbsp;... Statistics, when used in a misleading fashion, can trick the casual observer into 
believing ... When the statistical reason involved <b>is false</b> or misapplied, this 
constitutes a statistical ... <b>Multivariable</b> datasets have <b>two</b> or more features/
dimensions. ... of finding a <b>correlation between</b> any <b>two</b> sets of completely 
random <b>variables</b>. In econometrics, endogeneity broadly refers to situations in which an explanatory 
<b>variable</b> is <b>correlated</b> with the error term. The distinction <b>between</b> endogenous 
and exogenous variables originated in ... Suppose that <b>two variables</b> are 
codetermined, with each affecting the other according to the following &quot;structural&quot;
&nbsp;... The design of experiments is the design of any task that aims to describe and 
explain the ... The experimental design may also identify control <b>variables</b> that 
must be held ... There are <b>multiple</b> approaches for determining the set of design 
points ... the conditions that <b>causes</b> the differences in outcomes, that is - a <b>third</b> 
<b>variable</b>. In statistics, a confounder is a <b>variable</b> that influences both the dependent 
<b>variable</b> and independent <b>variable</b>, <b>causing</b> a spurious association. Confounding 
is a causal concept, and as such, cannot be described in terms of <b>correlations</b> or 
associations. ... Lastly, the relationship <b>between</b> the environmental <b>variables</b> that 
possibly&nbsp;... Causality is efficacy, by which one event, process or state, a <b>cause</b>, contributes to 
the ... <b>Causes</b> may sometimes be distinguished into <b>two</b> types: necessary and ... 
<b>between</b> values of <b>variables</b>, but a function of one <b>variable</b> (the <b>cause</b>) on to ... 
This helps to avoid <b>false</b> inferences of causality <b>due to</b> the presence of a <b>third</b>,&nbsp;...
lurking variable
Correlation does not imply causation