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  • A song for use in helping students to recognize and construct examples to illustrate how correlation does not imply causation.  Music & Lyrics © 2016 by Monty Harper.  This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).

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  • A song for use in helping students to recognize and construct examples to illustrate how correlation does not imply causation.  Music & Lyrics © 2016 by Monty Harper.  This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).

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  • A song for use in helping students to recognize, in context, parts of a chi-squared test for independence (null, degrees of freedom, and observed versus expected rationale. Lyrics © 2015 by Dominic Dousa and Lawrence M. Lesser, Music © 2015 by Dominic Dousa  from The University of Texas at El Paso.  This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).

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  • A song for use in helping students to recognize when the Central Limit Theorem applies.  Music & Lyrics ©2016 by Greg Crowther from Everett Community College. This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smilesfor the interactive version of the song, a short reading covering the topic, and an assessment item).

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  • A song for use in helping students to recognize, in context, the idea of ANOVA as comparing variance between groups to variance within groups.  Music & Lyrics © 2016 by Monty Harper.  This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).

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  • A song for use in helping students to understand that standard error changes with the square root of the sample size.  Music & Lyrics by Tom Toce, © 2015 Retrograde Music.  This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).

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  • A song for use in helping students to apply relationships among alpha, p-value, and the decision of a hypothesis test.  Music & Lyrics © 2015 by Lawrence M. Lesser from The University of Texas at El Paso.  This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).

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  • A joke to use in talking about how simulation might aid in learning statistical inference.  The joke was written in April 2018 by Dennis Pearl from Penn State University with editorial help from Larry Lesser from University of Texas at El Paso.

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  • A joke to start a discussion on joint probability distributions.  The joke was written in 2018 by Larry Lesser from The University of Texas at El Paso.

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  • "There are a lot of small data problems that occur in big data.  They don't disappear because you've got lots of stuff.  They get worse." is a quote by British biostatistician David J. Spiegelhalter (1953 - ).  The quote may be found in a March 28, 2014 article in the Financial Times written by Tim Hartford entitled "Big data: are we making a big mistake?"

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