#statisticalliteracy — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #statisticalliteracy, aggregated by home.social.
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Die Berichterstattung zur Polizeilichen Kriminalstatistik ist (erneut) ein dramatischer intellektueller Tiefflieger, unbenommen der bewussten Fehldeutungen einschlägiger Lager.
Es bleibt zu hoffen, dass die Autoren die prägnanten statistischen Effekte stärker in den Vordergrund rücken, um falsche Instrumentalisierungen zu erschweren.
Guter Artikel dazu: https://www.volksverpetzer.de/analyse/auslaenderkriminalitaet-geht-zurueck/
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Die Berichterstattung zur Polizeilichen Kriminalstatistik ist (erneut) ein dramatischer intellektueller Tiefflieger, unbenommen der bewussten Fehldeutungen einschlägiger Lager.
Es bleibt zu hoffen, dass die Autoren die prägnanten statistischen Effekte stärker in den Vordergrund rücken, um falsche Instrumentalisierungen zu erschweren.
Guter Artikel dazu: https://www.volksverpetzer.de/analyse/auslaenderkriminalitaet-geht-zurueck/
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Die Berichterstattung zur Polizeilichen Kriminalstatistik ist (erneut) ein dramatischer intellektueller Tiefflieger, unbenommen der bewussten Fehldeutungen einschlägiger Lager.
Es bleibt zu hoffen, dass die Autoren die prägnanten statistischen Effekte stärker in den Vordergrund rücken, um falsche Instrumentalisierungen zu erschweren.
Guter Artikel dazu: https://www.volksverpetzer.de/analyse/auslaenderkriminalitaet-geht-zurueck/
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Die Berichterstattung zur Polizeilichen Kriminalstatistik ist (erneut) ein dramatischer intellektueller Tiefflieger, unbenommen der bewussten Fehldeutungen einschlägiger Lager.
Es bleibt zu hoffen, dass die Autoren die prägnanten statistischen Effekte stärker in den Vordergrund rücken, um falsche Instrumentalisierungen zu erschweren.
Guter Artikel dazu: https://www.volksverpetzer.de/analyse/auslaenderkriminalitaet-geht-zurueck/
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Die Berichterstattung zur Polizeilichen Kriminalstatistik ist (erneut) ein dramatischer intellektueller Tiefflieger, unbenommen der bewussten Fehldeutungen einschlägiger Lager.
Es bleibt zu hoffen, dass die Autoren die prägnanten statistischen Effekte stärker in den Vordergrund rücken, um falsche Instrumentalisierungen zu erschweren.
Guter Artikel dazu: https://www.volksverpetzer.de/analyse/auslaenderkriminalitaet-geht-zurueck/
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Love occasionally stumbling across this site. Spurious — Beautifully Meaningless Correlations
A platform dedicated to making statistical literacy fun. We find surprising correlations between completely unrelated datasets — real data, ridiculous connections, zero causation.
We analyze over 300 datasets across 13 categories. This produces over 16,000 statistically strong correlations, with the top 2,200 published as pages on this site.
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Love occasionally stumbling across this site. Spurious — Beautifully Meaningless Correlations
A platform dedicated to making statistical literacy fun. We find surprising correlations between completely unrelated datasets — real data, ridiculous connections, zero causation.
We analyze over 300 datasets across 13 categories. This produces over 16,000 statistically strong correlations, with the top 2,200 published as pages on this site.
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Love occasionally stumbling across this site. Spurious — Beautifully Meaningless Correlations
A platform dedicated to making statistical literacy fun. We find surprising correlations between completely unrelated datasets — real data, ridiculous connections, zero causation.
We analyze over 300 datasets across 13 categories. This produces over 16,000 statistically strong correlations, with the top 2,200 published as pages on this site.
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Love occasionally stumbling across this site. Spurious — Beautifully Meaningless Correlations
A platform dedicated to making statistical literacy fun. We find surprising correlations between completely unrelated datasets — real data, ridiculous connections, zero causation.
We analyze over 300 datasets across 13 categories. This produces over 16,000 statistically strong correlations, with the top 2,200 published as pages on this site.
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Love occasionally stumbling across this site. Spurious — Beautifully Meaningless Correlations
A platform dedicated to making statistical literacy fun. We find surprising correlations between completely unrelated datasets — real data, ridiculous connections, zero causation.
We analyze over 300 datasets across 13 categories. This produces over 16,000 statistically strong correlations, with the top 2,200 published as pages on this site.
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Seven simple questions for #DecisionMakers:
1️⃣ How big? How much? How many?
2️⃣ Compared to what?
3️⃣ Why not a rate?
4️⃣ Per what? The diabolical denominator.
5️⃣ How were things defined, counted or measured?
6️⃣ What was taken into account (what was controlled for)?
7️⃣ What else should have been taken into account (controlled for)?Source:
➡️ http://www.statlit.org/pdf/2022-Schield-SJIAOS.pdf#CriticalThinking #Statistics #StatisticalLiteracy #InformationLiteracy #DataLiteracy #Data #Policy #Politics #Business #LabPlot
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Seven simple questions for #DecisionMakers:
1️⃣ How big? How much? How many?
2️⃣ Compared to what?
3️⃣ Why not a rate?
4️⃣ Per what? The diabolical denominator.
5️⃣ How were things defined, counted or measured?
6️⃣ What was taken into account (what was controlled for)?
7️⃣ What else should have been taken into account (controlled for)?Source:
➡️ http://www.statlit.org/pdf/2022-Schield-SJIAOS.pdf#CriticalThinking #Statistics #StatisticalLiteracy #InformationLiteracy #DataLiteracy #Data #Policy #Politics #Business #LabPlot
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Seven simple questions for #DecisionMakers:
1️⃣ How big? How much? How many?
2️⃣ Compared to what?
3️⃣ Why not a rate?
4️⃣ Per what? The diabolical denominator.
5️⃣ How were things defined, counted or measured?
6️⃣ What was taken into account (what was controlled for)?
7️⃣ What else should have been taken into account (controlled for)?Source:
➡️ http://www.statlit.org/pdf/2022-Schield-SJIAOS.pdf#CriticalThinking #Statistics #StatisticalLiteracy #InformationLiteracy #DataLiteracy #Data #Policy #Politics #Business #LabPlot
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Seven simple questions for #DecisionMakers:
1️⃣ How big? How much? How many?
2️⃣ Compared to what?
3️⃣ Why not a rate?
4️⃣ Per what? The diabolical denominator.
5️⃣ How were things defined, counted or measured?
6️⃣ What was taken into account (what was controlled for)?
7️⃣ What else should have been taken into account (controlled for)?Source:
➡️ http://www.statlit.org/pdf/2022-Schield-SJIAOS.pdf#CriticalThinking #Statistics #StatisticalLiteracy #InformationLiteracy #DataLiteracy #Data #Policy #Politics #Business #LabPlot
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Seven simple questions for #DecisionMakers:
1️⃣ How big? How much? How many?
2️⃣ Compared to what?
3️⃣ Why not a rate?
4️⃣ Per what? The diabolical denominator.
5️⃣ How were things defined, counted or measured?
6️⃣ What was taken into account (what was controlled for)?
7️⃣ What else should have been taken into account (controlled for)?Source:
➡️ http://www.statlit.org/pdf/2022-Schield-SJIAOS.pdf#CriticalThinking #Statistics #StatisticalLiteracy #InformationLiteracy #DataLiteracy #Data #Policy #Politics #Business #LabPlot
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"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight."
Longford (2005) http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf#modeling #nullHypothesis #probability #probabilities #pValues #statistics #stats #statisticalLiteracy #bias #inference #modelling #regression #linearRegression
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"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight."
Longford (2005) http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf#modeling #nullHypothesis #probability #probabilities #pValues #statistics #stats #statisticalLiteracy #bias #inference #modelling #regression #linearRegression
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"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight."
Longford (2005) http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf#modeling #nullHypothesis #probability #probabilities #pValues #statistics #stats #statisticalLiteracy #bias #inference #modelling #regression #linearRegression
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"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight."
Longford (2005) http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf#modeling #nullHypothesis #probability #probabilities #pValues #statistics #stats #statisticalLiteracy #bias #inference #modelling #regression #linearRegression
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"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight."
Longford (2005) http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf#modeling #nullHypothesis #probability #probabilities #pValues #statistics #stats #statisticalLiteracy #bias #inference #modelling #regression #linearRegression
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Statistical #consulting guidelines for #EarlyCareerResearchers in #psychiatry and mental health– beyond ChatGPT. Joint work with Elise Dusseldorp at Leiden University published today in @thebjpsych (BJPsych Advances) | Read it here in full: https://bit.ly/3ZpjMd8
Feel free to share it and your feedback is welcome.
#Statistics #Research #methodology #statisticalliteracy #mentalhealthprofessionals -
Surveys, coincidences, statistical significance 🧵
"What Educated Citizens Should Know About Statistics and Probability"
By Jessica Utts, in 2003: https://ics.uci.edu/~jutts/AmerStat2003.pdf via @hrefna#nullHypothesis #probability #probabilities #pValues #statistics #stats #education #higherEd #statisticalLiteracy #bias #media #causalInference
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Surveys, coincidences, statistical significance 🧵
"What Educated Citizens Should Know About Statistics and Probability"
By Jessica Utts, in 2003: https://ics.uci.edu/~jutts/AmerStat2003.pdf via @hrefna#nullHypothesis #probability #probabilities #pValues #statistics #stats #education #higherEd #statisticalLiteracy #bias #media #causalInference
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Surveys, coincidences, statistical significance 🧵
"What Educated Citizens Should Know About Statistics and Probability"
By Jessica Utts, in 2003: https://ics.uci.edu/~jutts/AmerStat2003.pdf via @hrefna#nullHypothesis #probability #probabilities #pValues #statistics #stats #education #higherEd #statisticalLiteracy #bias #media #causalInference
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Surveys, coincidences, statistical significance 🧵
"What Educated Citizens Should Know About Statistics and Probability"
By Jessica Utts, in 2003: https://ics.uci.edu/~jutts/AmerStat2003.pdf via @hrefna#nullHypothesis #probability #probabilities #pValues #statistics #stats #education #higherEd #statisticalLiteracy #bias #media #causalInference
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Surveys, coincidences, statistical significance 🧵
"What Educated Citizens Should Know About Statistics and Probability"
By Jessica Utts, in 2003: https://ics.uci.edu/~jutts/AmerStat2003.pdf via @hrefna#nullHypothesis #probability #probabilities #pValues #statistics #stats #education #higherEd #statisticalLiteracy #bias #media #causalInference
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5/ Meditation time 🤔 Another of Paul F. Velleman's Fourteen Data Aphorisms for Data Analysis to contemplate on:
#Aphorism #DataAnalysis #DataScience #Data #DataViz #Science #Statistics #CriticalThinking #STEM #StatisticalLiteracy #LabPlot #FOSS #FLOSS #OpenSource
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5/ Meditation time 🤔 Another of Paul F. Velleman's Fourteen Data Aphorisms for Data Analysis to contemplate on:
#Aphorism #DataAnalysis #DataScience #Data #DataViz #Science #Statistics #CriticalThinking #STEM #StatisticalLiteracy #LabPlot #FOSS #FLOSS #OpenSource
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5/ Meditation time 🤔 Another of Paul F. Velleman's Fourteen Data Aphorisms for Data Analysis to contemplate on:
#Aphorism #DataAnalysis #DataScience #Data #DataViz #Science #Statistics #CriticalThinking #STEM #StatisticalLiteracy #LabPlot #FOSS #FLOSS #OpenSource
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5/ Meditation time 🤔 Another of Paul F. Velleman's Fourteen Data Aphorisms for Data Analysis to contemplate on:
#Aphorism #DataAnalysis #DataScience #Data #DataViz #Science #Statistics #CriticalThinking #STEM #StatisticalLiteracy #LabPlot #FOSS #FLOSS #OpenSource
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5/ Meditation time 🤔 Another of Paul F. Velleman's Fourteen Data Aphorisms for Data Analysis to contemplate on:
#Aphorism #DataAnalysis #DataScience #Data #DataViz #Science #Statistics #CriticalThinking #STEM #StatisticalLiteracy #LabPlot #FOSS #FLOSS #OpenSource
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In my humble opinion, this book should be a must-read in all classes of quantitative methodology in social sciences (and not only). #StatisticalLiteracy #dataanalysis #rstats @sociology
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In my humble opinion, this book should be a must-read in all classes of quantitative methodology in social sciences (and not only). #StatisticalLiteracy #dataanalysis #rstats @sociology
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In my humble opinion, this book should be a must-read in all classes of quantitative methodology in social sciences (and not only). #StatisticalLiteracy #dataanalysis #rstats @sociology
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In my humble opinion, this book should be a must-read in all classes of quantitative methodology in social sciences (and not only). #StatisticalLiteracy #dataanalysis #rstats @sociology
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In my humble opinion, this book should be a must-read in all classes of quantitative methodology in social sciences (and not only). #StatisticalLiteracy #dataanalysis #rstats @sociology
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@ZeroCovidColin While short of the PROOF you demand, you probably know this excellent RCT on a related topic? ✈️ 🪂 🤣
Parachute use to prevent death and major trauma when jumping from aircraft: randomized controlled trial
https://www.bmj.com/content/363/bmj.k5094#parachute #RCT #EBM
#stats #statistics #StatisticalLiteracy
#Covid #Covid19 #EvidencePluralism -
@ZeroCovidColin While short of the PROOF you demand, you probably know this excellent RCT on a related topic? ✈️ 🪂 🤣
Parachute use to prevent death and major trauma when jumping from aircraft: randomized controlled trial
https://www.bmj.com/content/363/bmj.k5094#parachute #RCT #EBM
#stats #statistics #StatisticalLiteracy
#Covid #Covid19 #EvidencePluralism -
@ZeroCovidColin While short of the PROOF you demand, you probably know this excellent RCT on a related topic? ✈️ 🪂 🤣
Parachute use to prevent death and major trauma when jumping from aircraft: randomized controlled trial:
https://www.bmj.com/content/363/bmj.k5094#parachute #RCT #EBM
#stats #statistics #StatisticalLiteracy #EvidencePluralism -
@ZeroCovidColin While short of the PROOF you demand, you probably know this excellent RCT on a related topic? ✈️ 🪂 🤣
Parachute use to prevent death and major trauma when jumping from aircraft: randomized controlled trial
https://www.bmj.com/content/363/bmj.k5094#parachute #RCT #EBM
#stats #statistics #StatisticalLiteracy
#Covid #Covid19 #EvidencePluralism -
@ZeroCovidColin While short of the PROOF you demand, you probably know this excellent RCT on a related topic? ✈️ 🪂 🤣
Parachute use to prevent death and major trauma when jumping from aircraft: randomized controlled trial
https://www.bmj.com/content/363/bmj.k5094#parachute #RCT #EBM
#stats #statistics #StatisticalLiteracy
#Covid #Covid19 #EvidencePluralism -
Acc. to Milo Schield :
"The main thing is for policymakers to treat statistics the same way they treat people. People have motives, values and agendas. So do statistics – because they were selected, assembled and presented by people who have motives, values and agendas. Statistics are closer to words than to numbers. Yes, statistics involve numbers, but statistics are numbers in context and the words give the context."
#Statistics @StatisticalThinking #StatisticalLiteracy
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The Seven Unnatural Acts of Statistical Thinking acc. to Richard D. De Veaux and Paul F. Velleman
1. Think Critically.
2. Be Skeptical. Question authority and the current theory.
3. Think about variation rather than about center.
4. Focus on what we don’t know.
5. Perfect the Process. Our best conclusion is often a refined question.
6. Think about conditional probabilities and rare events.
7. Embrace vague concepts (Center, Outlier, Linear...). -
The Seven Unnatural Acts of Statistical Thinking acc. to Richard D. De Veaux and Paul F. Velleman
1. Think Critically.
2. Be Skeptical. Question authority and the current theory.
3. Think about variation rather than about center.
4. Focus on what we don’t know.
5. Perfect the Process. Our best conclusion is often a refined question.
6. Think about conditional probabilities and rare events.
7. Embrace vague concepts (Center, Outlier, Linear...). -
The Seven Unnatural Acts of Statistical Thinking acc. to Richard D. De Veaux and Paul F. Velleman
1. Think Critically.
2. Be Skeptical. Question authority and the current theory.
3. Think about variation rather than about center.
4. Focus on what we don’t know.
5. Perfect the Process. Our best conclusion is often a refined question.
6. Think about conditional probabilities and rare events.
7. Embrace vague concepts (Center, Outlier, Linear...). -
The Seven Unnatural Acts of Statistical Thinking acc. to Richard D. De Veaux and Paul F. Velleman
1. Think Critically.
2. Be Skeptical. Question authority and the current theory.
3. Think about variation rather than about center.
4. Focus on what we don’t know.
5. Perfect the Process. Our best conclusion is often a refined question.
6. Think about conditional probabilities and rare events.
7. Embrace vague concepts (Center, Outlier, Linear...). -
The Seven Unnatural Acts of Statistical Thinking acc. to Richard D. De Veaux and Paul F. Velleman
1. Think Critically.
2. Be Skeptical. Question authority and the current theory.
3. Think about variation rather than about center.
4. Focus on what we don’t know.
5. Perfect the Process. Our best conclusion is often a refined question.
6. Think about conditional probabilities and rare events.
7. Embrace vague concepts (Center, Outlier, Linear...).