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Text of the page (random words):
osen randomly this is however a misunderstanding randomness and representativeness are two fundamentally distinct properties one describes a procedure the other describes an outcome they can and often do come apart let us consider a simple classroom experiment a teacher has a group of 20 children 10 boys and 10 girls the teacher asks the children whether they prefer sausages or ice cream and wants to select a small group of six children whose opinions can stand for the whole class she first uses a purely random method she writes all 20 names on slips of paper places them in a hat and draws six names without looking by chance the draw yields five boys and only one girl suppose that in this class the boys strongly prefer sausages while the girls overwhelmingly prefer ice cream the randomly selected group with its heavy male majority ends up favouring sausages by a large margin even though the class as a whole given its equal gender split might actually prefer ice cream this sample was random in every technical sense each child had an equal chance of being selected and chance alone determined the outcome yet it is clearly not representative of the class as a whole because it fails to reflect the crucial gender balance that shapes the underlying preference distribution randomness provides no guarantee of representativeness it merely ensures equal probability of selection and probabilities do not always produce proportional outcomes in a single draw now consider the opposite case the teacher adopts a different approach instead of drawing names from a hat she deliberately selects three boys and three girls to match the class s 50 50 gender split to do this she simply chooses the first three boys and the first three girls who raise their hands this sample perfectly mirrors the class in terms of gender and since gender is the main factor influencing food preference the sample s opinions will closely track those of the whole class it is in other words highly representative yet the procedure is clearly not random the teacher does not use chance she applies a conscious non probabilistic rule furthermore she only samples children who happen to raise their hands quickly which may exclude distracted or shy pupils thus a representative sample has been produced through a non random method demonstrating that representativeness does not require randomness what these two cases show is that randomness and representativeness answer different questions randomness tells us how a sample is selected it is a feature of the selection mechanism representativeness tells us how the sample looks like it is a feature of the sample s composition a random sample may by chance fail to capture those characteristics conversely a carefully constructed quota sample may capture them accurately without any use of chance in the end both properties are valuable for different reasons randomness is important because it enables statistical inference we can calculate margins of error and confidence intervals because the selection process is known representativeness is important because it gives us confidence that our conclusions apply to the population of interest the ideal study combines both but as my sausage and ice cream example shows in practice these are two distinct ideals a random sample can be unrepresentative and a representative sample can be non random confusing the two is a common error but it remains an error nonetheless share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky lönebildning och arbetslöshet i sverige 17 jun 2026 at 09 51 posted in economics 14 comments i ekonomisk debatt 4 2026 lyfter ekonomiprofessor pär österholm den höga svenska arbetslösheten som han menar har strukturella orsaker konjunkturinstitutet uppskattar den så kallade jämviktsarbetslösheten till 7 3 procent det är denna nivå österholm tar som utgångspunkt jämviktsarbetslösheten är avgörande i österholms text eftersom det är den som påvisar problemets vidd ändå accepterar han okritiskt begreppet och konjunkturinstitutets nivå det är alltså så lågt arbetslösheten antas kunna pressas innan den blir inflationsdrivande men jämviktsarbetslösheten går inte att observera det är en teoretisk konstruktion på mycket skakig grund felmarginalen är därtill stor och antagandena har svag verklighetsförankring värre är att en felaktigt hög uppskattning kan bli självuppfyllande om finans och penningpolitiken stramar åt för tidigt skapas hysteresis att arbetslösheten biter sig fast frågan är om en viktigare förklaring till den höga arbetslösheten snarare är just den teoribildning som österholm bekänner sig till daniel lind german bender med tanke på det nästintill obefintliga empisk statistiska stöd som jämviktsarbetslöshets begreppet med rötter i milton friedmans naturliga arbetslöshet och nairu har är det minst sagt anmärkningsvärt att mainstreamekonomer fortsätter använda det det är svårt att undgå misstanken att det till stor del handlar om ideologiska motiv med inställningen att full sysselsättning är omöjlig att uppnå kan högerpolitiska förslag om fortsatt nedmontering av det svenska välfärdssamhället och åtstramningspolitik ges ett skimmer av vetenskaplighet om man skalar bort den övliga tekniska mumbo jumbon som vi nationalekonomer så ofta tyvärr hänger oss åt kvarstår det enkla faktum att det som verkligen styr den på vaga och ifrågasättbara grunder beräknade jämviktsarbetslösheten är den faktiskt uppmätta arbetslösheten varför konjunkturinstitut riksbank och finansdepartement då ska lägga en massa pengar och datortid på att kalkylera denna jämviktsarbetslöshet är svårt att förstå bland de som likväl försvarar jämviktsarbetsbegreppet brukar det ofta framföras att det trots sina brister är en bra tankeram verkligen nairu och andra jämviktsarbetslöshetsbegrepp fungerar i själva verket inte alls bra som tankeram i själva verket fungerar de som inte minst senare tids forskning av exempelvis engelbert stockhammer 2011 özlem onaran 2012 roger farmer 2010 storm naastepad 2012 och andra övertygande visat monumentalt dåligt som just tankeram så varför detta fasthållande vid ett begrepp som alla inklusive dess försvarare medger är dåligt teoretiskt underbyggt och empiriskt nästintill omöjligt att skatta i vanliga fall brukar vi inom vetenskapen kassera icke falsifierbara teorier det är hög tid att också göra det med jämviktsarbetslöshetsbegreppet share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky nancy cartwright s contributions to the philosophy of science 16 jun 2026 at 11 21 posted in theory of science methodology 1 comment alongside roy bhaskar and tony lawson nancy cartwright is probably the philosopher of science who has had the greatest influence on my thinking in this area cartwright and yours truly firmly committed to critical realism share a deep commitment to depth realism we reject the flat humean empiricism that reduces causality to nothing more than regular associations between observable events instead we maintain that reality is stratified beneath observable phenomena lie real causal powers and mechanisms that may or may not be activated depending on the circumstances cartwright s distinction between closed and open systems closely parallels my own understanding in closed systems such as carefully controlled experiments regularities can be artificially generated and sustained the open systems that characterise most of the real world however are governed by the interaction of multiple mechanisms making simple law like predictions difficult and often impossible like critical realists more generally cartwright sees the task of science as identifying the generative mechanisms that produce observed phenomena rather than searching for universal constant conjunctions her conception of a dappled world in which laws are local contingent and context dependent is entirely compatible with my own ontological commitments and my rejection of reductionism we also share a methodological affinity in our use of transcendental reasoning asking what the world must be like for scientific knowledge and practice to be possible in the first place in this respect cartwright has always been an important intellectual ally in the effort to move beyond positivism and towards a genuinely realist philosophy of science as cartwright states in the interview her interest in science lies not only in understanding the world but also in transforming it i wholeheartedly share this commitment as a staunch defender of bhaskar s concept of explanatory critique i would argue that identifying social structures that generate suffering and harm is never a neutral act of understanding rather it is the recognition of conditions that violate human flourishing and intrinsic worth a recognition that carries with it an imperative to act this is not merely an ethical recommendation but a rational necessity scientific critique therefore must form an integral part of any emancipatory project demanding active intervention to remedy the injustices and harms it reveals share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky what does randomisation guarantee nothing 15 jun 2026 at 17 15 posted in statistics econometrics leave a comment the point of conducting a randomised experiment is often said to be that it allows researchers to make stronger causal inferences from observed correlations this claim rests on the idea that randomisation reduces systematic differences between treatment and control groups thereby limiting the influence of confounding factors the problem with this rather simplistic view of randomisation is that the claims made on its behalf are both exaggerated and strictly speaking false even if the assignment of subjects to treatment and control groups is perfectly random sample selection almost certainly is not except in extremely rare cases proper random assignment does not compensate for a biased sample if experimental results are derived from a non representative sample there is always a risk that the findings will not generalise beyond that setting what works there may not work here randomisation therefore does not guarantee or ensure that we arrive at the correct causal conclusion while it may help us rule out certain alternative explanations randomisation in itself guarantees nothing even if both sampling and assignment were carried out in an ideally random manner standard randomised experiments provide only average effects the difficulty is that an estimate of the true average causal effect may conceal important heterogeneity the average treatment effect may be zero while some individuals experience effects of 100 and others effects of 100 for anyone considering whether to undergo treatment such heterogeneity is likely to be far more informative than the average effect alone there is almost always a trade off between bias and precision in real world settings a small amount of bias may be more than offset by a substantial gain in precision more importantly when a population exhibits significant heterogeneity the average treatment effect estimated from a sample may differ considerably from the average treatment effect in the wider population if that is the case the value of extrapolating findings from trial samples to other populations becomes highly questionable since most real world experiments and trials involve only a single randomisation the properties that would emerge over an infinite sequence of repeated randomisations provide little reassurance about the validity of the one experiment that is actually conducted it is difficult to see why knowledge of what would happen under circumstances that will never occur should make us more confident about the conclusions drawn from the single study we have performed the problem faced by many randomistas when they underestimate heterogeneity and interaction effects is not merely one of external validity when attempting to transfer regression results across periods contexts or populations it is also an internal problem affecting the millions of regression estimates produced by economists each year ideally controlled experiments can tell us with certainty what causes what effects but only given the appropriate closure assumptions making valid extrapolations from experiments whether ideal accidental natural or quasi experimental to different settings populations or target systems is far from straightforward since trials are rarely repeated properties such as unbiasedness and balance in repeated sampling tell us nothing directly about any particular trial it works there is not evidence that it will work here causes identified in an experimental setting must still be shown to possess an export warrant to the target population or system the underlying causal assumptions must be justified and without such a warrant the value of even the most rigorous and precise methods and of knowledge that holds only on average is severely limited rcts have little reach beyond describing what has occurred in the past from the perspective of future decision making and public policy they are often of limited value because they cannot tell us which background factors were held constant when the intervention was implemented nor do rcts generally provide evidence that their results are exportable to other target systems they cannot simply be assumed to yield generalisable findings the fact that something works somewhere for someone is no guarantee that it will work for us here or indeed that it works more generally randomisation may in the right circumstances help us draw causal conclusions however it is neither necessary for establishing causality nor sufficient for securing scientific validity randomisation guarantees nothing just as observational studies are vulnerable to various forms of bias so too are randomised studies and trials share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky model reification in statistics and econometrics 15 jun 2026 at 10 57 posted in economics statistics econometrics 2 comments conventional statistical methods assume that within measured covariate levels the data come from surveys with purely random selection from a sharply defined population or from experiments with purely random treatment assignment in which the researcher has complete control and understanding of every important aspect of those who entered the analysis and how they were treated the methods thus provide logically sound inferences only in ideal cases rather than correct answers for our actual imperfect applications they are however widely applied and misinterpreted in studies that fall far short of their assumptions we may view each statistical analysis as a thought experiment in a fictional small world or...
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