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Watch in slide show mode to observe (modest) animation.

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1 Watch in slide show mode to observe (modest) animation.
comments questions: papers, etc:

2 Some Grist for your Graphic-reporting Mill
Dan M. Kahan Professor, Yale University Affiliated Researcher, Annenberg Public Policy Center

3 First: the pure, unadulterated evil of bar charts . . .

4

5

6 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree Modestly disagree Slightly agree Slightly disagree moderately disagree Strongly disagree Lib. Dem Con. Rep. Lib. Dem Con. Rep. Lib. Dem Con. Rep.

7 Next: good old scatter plots, and good new (relatively, at least) locally weighted regression plots. . .

8 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree “believer biased” Modestly disagree control “skeptic biased” Slightly agree Slightly disagree moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

9 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree “believer biased” Modestly disagree control “skeptic biased” Slightly agree Slightly disagree moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

10 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree “believer biased” Modestly disagree control “skeptic biased” Slightly agree Slightly disagree moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

11 CRT = 0 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree “believer biased” Modestly disagree control “skeptic biased” Slightly agree Slightly disagree moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

12 CRT = 0 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree “believer biased” Modestly disagree control “skeptic biased” Slightly agree Slightly disagree moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

13 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree “believer biased” Modestly disagree control “skeptic biased” Slightly agree Slightly disagree moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

14 CRT = 0 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree “believer biased” Modestly disagree control “skeptic biased” Slightly agree Slightly disagree moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

15 CRT = 1 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree “believer biased” Modestly disagree control “skeptic biased” Slightly agree Slightly disagree moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

16 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree “believer biased” Modestly disagree control “skeptic biased” Slightly agree Slightly disagree moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

17 CRT = 1 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree “believer biased” Modestly disagree control “skeptic biased” Slightly agree Slightly disagree moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

18 CRT > 1 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree “believer biased” Modestly disagree control “skeptic biased” Slightly agree Slightly disagree moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

19 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree “believer biased” Modestly disagree control “skeptic biased” Slightly agree Slightly disagree moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

20 CRT = 0 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree “believer biased” Modestly disagree control “skeptic biased” Slightly agree Slightly disagree moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

21 CRT = 0 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree Modestly disagree Slightly agree control “believer biased” Slightly disagree “skeptic biased” moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

22 CRT = 1 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree “believer biased” Modestly disagree control “skeptic biased” Slightly agree Slightly disagree moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

23 CRT = 1 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree Modestly disagree Slightly agree “believer biased” Slightly disagree control moderately disagree “skeptic biased” Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

24 CRT > 1 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree “believer biased” Modestly disagree control “skeptic biased” Slightly agree Slightly disagree moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

25 CRT > 1 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree Modestly disagree Slightly agree “believer biased” Slightly disagree control “skeptic biased” moderately disagree Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right

26 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree Modestly disagree Slightly agree “believer biased” Slightly disagree control moderately disagree “skeptic biased” Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right CRT = 0

27 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree Modestly disagree Slightly agree “believer biased” Slightly disagree control moderately disagree “skeptic biased” Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right CRT = 1

28 “I think the word-problem test I just took supplies good evidence of how reflective and open-minded someone is.” Strongly agree Modestly disagree Slightly agree “believer biased” Slightly disagree control moderately disagree “skeptic biased” Strongly disagree Very liberal Strong Democrat Liberal Democrat Moderate Independent Conservative Republican Very Conservative Strong Republican Left right CRT > 1

29

30

31 Tyranny of the regression output . . . .

32

33 control “skeptic biased” “believer biased” Lib. Dem. low CRT Con. Rep.
high CRT Con. Rep. high CRT

34

35 Now: The pure, unadulterated goodness of Monte Carlo simulations

36

37 “Skin cream experiment”

38 “Skin cream experiment”

39 “Gun ban experiment”

40 Four conditions

41 rash increases rash decreases

42 rash decreases rash increases

43 Covariance & Numeracy rash decreases rash increases
Derived via logistic regression. Bars denote 0.95 CIs.

44 Correct interpretation of data
skin treatment Numeracy score Gun ban Numeracy score

45 Correct interpretation of data
Liberal Democrats (< 0 on Conservrepub) Conserve Republicans (> 0 on Conservrepub) skin treatment Numeracy score Gun ban Numeracy score

46 Correct interpretation of data
Liberal Democrats (< 0 on Conservrepub) Conserve Republicans (> 0 on Conservrepub) skin treatment Numeracy score Gun ban Numeracy score

47 Correct interpretation of data
Liberal Democrats (< 0 on Conservrepub) Conserve Republicans (> 0 on Conservrepub) skin treatment Numeracy score Gun ban Numeracy score

48 Regression model for experiment results
rash_decrease 0.40 (1.57) rash increase 0.06 (0.22) crime increase 1.07 (4.02) z_numeracy -0.01 (-0.05) z_numeracy_x_rash_decrease 0.55 (2.29) z_numeracy_x_rash_increase 0.23 (1.05) z_numeracy_x_crime_increase 0.46 (2.01) z_numeracy2 0.31 (2.46) z_numeracy2_x_rash_decrease 0.02 (0.14) z_numeracy2_x_rash_increase -0.07 (-0.39) z_numeracy2_x_crime_increase -0.31 (-1.75) Conserv_Repub -0.64 (-3.95) Conserv_Repub_x_rash_decrease 0.56 (2.64) Conserv_Repub_x_rash_increase 1.28 (6.02) Conserv_Repub_x_crime_increase 0.63 (2.82) z_numeracy_x_Conserv_repub -0.33 (-1.89) z_nuneracy_x_Conserv_Repub_x_rash_decrease 0.33 (1.40) z_nuneracy_x__crime_increase 0.54 (2.17) z_nuneracy_x__x_rash_increase 0.26 (1.08) _constant -0.96 (-4.70) N = Outcome variable is “Correct” (0 = incorrect interpretation of data, 1 = correct interpretation). Predictor estimates are logit coefficients with z-test statistic indicated parenthetically. Experimental assignment predictors—rash_decrease, rash_increase, and crime_increase—are dummy variables (0 = unassigned, 1 = assigned—with assignment to “crime decreases” as the comparison condition. Numeracy and Conserv_Repub are centered at 0 for ease of interpretation. Bolded typeface indicates predictor coefficient is significant at p < 0.05.

49 Monte Carlo simulations
Liberal Democrat (-1 SD on Conservrepub) high numeracy = 7 correct low numeracy = 3 correct Conserv Republican (+1 SD on Conservrepub) Low numeracy High numeracy rash increases skin treatment rash increases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% crime increases 80% 90% 100% crime decreases Gun ban crime increases crime decreases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% probabilility of correct interpretation of data probabilility of correct interpretation of data

50 Monte Carlo simulations
Liberal Democrat (-1 SD on Conservrepub) high numeracy = 7 correct low numeracy = 3 correct Conserv Republican (+1 SD on Conservrepub) Low numeracy High numeracy rash increases 25%, ± 10 skin treatment rash increases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% crime increases 80% 90% 100% crime decreases Gun ban crime increases crime decreases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% probabilility of correct interpretation of data probabilility of correct interpretation of data

51 Monte Carlo simulations
Liberal Democrat (-1 SD on Conservrepub) high numeracy = 7 correct low numeracy = 3 correct Conserv Republican (+1 SD on Conservrepub) Low numeracy High numeracy rash increases rash increases rash decreases skin treatment rash increases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% crime increases 80% 90% 100% crime decreases crime increases crime increases crime decreases crime decreases Gun ban crime increases crime decreases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% probabilility of correct interpretation of data probabilility of correct interpretation of data

52 Monte Carlo simulations
Liberal Democrat (-1 SD on Conservrepub) high numeracy = 7 correct low numeracy = 3 correct Conserv Republican (+1 SD on Conservrepub) Low numeracy High numeracy 5%, ± 6 rash increases rash increases rash decreases rash decreases rash decreases rash decrease rash decreases rash increases rash increases skin treatment 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% crime increases 80% 90% 100% crime decreases crime increases crime increases crime decreases crime decreases Gun ban crime increases crime decreases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% probabilility of correct interpretation of data probabilility of correct interpretation of data

53 Monte Carlo simulations
Liberal Democrat (-1 SD on Conservrepub) high numeracy = 7 correct low numeracy = 3 correct Conserv Republican (+1 SD on Conservrepub) Low numeracy High numeracy rash increases rash increases rash decreases rash decreases rash decreases rash decreases rash decreases rash increases rash increases skin treatment 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% crime increases 80% 90% 100% crime decreases crime increases crime increases crime decreases crime decreases Gun ban crime increases crime decreases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% probabilility of correct interpretation of data probabilility of correct interpretation of data

54 Monte Carlo simulations
Liberal Democrat (-1 SD on Conservrepub) high numeracy = 7 correct low numeracy = 3 correct Conserv Republican (+1 SD on Conservrepub) Low numeracy High numeracy rash increases rash increases rash decreases rash decreases rash decreases rash decreases rash decreases rash increases rash increases skin treatment 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% crime increases 80% 90% 100% crime decreases crime increases crime increases crime decreases crime decreases Gun ban crime increases crime decreases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% probabilility of correct interpretation of data probabilility of correct interpretation of data

55 Monte Carlo simulations
Liberal Democrat (-1 SD on Conservrepub) high numeracy = 7 correct low numeracy = 3 correct Conserv Republican (+1 SD on Conservrepub) Low numeracy High numeracy rash increases rash increases rash decreases rash decreases rash decreases rash decreases rash decreases rash increases rash increases skin treatment 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% crime increases 80% 90% 100% crime decreases crime increases crime increases crime increases crime increases crime increases crime decreases crime decreases Gun ban crime increases crime increases crime decreases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% probabilility of correct interpretation of data probabilility of correct interpretation of data

56 Monte Carlo simulations
Liberal Democrat (-1 SD on Conservrepub) high numeracy = 7 correct low numeracy = 3 correct Conserv Republican (+1 SD on Conservrepub) Low numeracy High numeracy rash increases rash increases rash decreases rash decreases rash decreases rash decreases rash decreases rash increases rash increases skin treatment 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% crime increases 80% 90% 100% crime decreases crime decreases crime increases crime increases crime decreases crime decreases crime decreases crime decreases crime decreases Gun ban crime increases crime decreases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% probabilility of correct interpretation of data probabilility of correct interpretation of data

57 Monte Carlo simulations
Liberal Democrat (-1 SD on Conservrepub) high numeracy = 7 correct low numeracy = 3 correct Conserv Republican (+1 SD on Conservrepub) Low numeracy High numeracy rash increases rash increases rash decreases rash decreases rash decreases rash decreases rash decreases rash increases rash increases skin treatment 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% crime increases 80% 90% 100% crime decreases crime decreases crime increases crime increases crime increases crime increases crime increases crime decreases crime decreases crime decreases crime decreases crime decreases Gun ban crime increases crime increases crime decreases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% probabilility of correct interpretation of data probabilility of correct interpretation of data

58 Monte Carlo simulations
Liberal Democrat (-1 SD on Conservrepub) high numeracy = 7 correct low numeracy = 3 correct Conserv Republican (+1 SD on Conservrepub) Low numeracy High numeracy Avg. “polarization” on crime data for low numeracy partisans 25% (± 9%) Avg. “polarization” on crime data for high numeracy partisans 46% (± 17%) crime decreases crime decreases crime increases crime increases crime increases crime increases crime increases crime decreases crime decreases crime decreases crime decreases crime decreases Gun ban crime increases crime increases crime decreases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% probabilility of correct interpretation of data probabilility of correct interpretation of data

59 Monte Carlo simulations
Liberal Democrat (-1 SD on Conservrepub) high numeracy = 7 correct low numeracy = 3 correct Conserv Republican (+1 SD on Conservrepub) Low numeracy High numeracy rash increases rash increases rash decreases rash decreases rash decreases rash decreases rash decreases rash increases rash increases skin treatment 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% crime increases 80% 90% 100% crime decreases crime decreases crime increases crime increases crime increases crime increases crime increases crime decreases crime decreases crime decreases crime decreases crime decreases Gun ban crime increases crime increases crime decreases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% probabilility of correct interpretation of data probabilility of correct interpretation of data

60 “Motivated Numeracy” results

61 “Motivated Numeracy” results

62 “Motivated Numeracy” results

63 “Motivated Numeracy” results

64 How about Bayesian Likelihood Ratios (LRs)?

65 How about Bayesian Likelihood Ratios (LRs)?
Prior odds X Likelihood ratio = Posterior odds

66 Bayes Likelihood Ratio: Measuring the Weight of the Evidence
H1: MS2R = 0.15 Probability density MS2R effect The probability distributions associated with the indicated hypotheses were constructed using the standard error (0.08) of the observed effect in the experiment. . The vertical line intersecting the probability distributions is the observed effect (0.11) in KPDS.

67 Monte Carlo simulations
Liberal Democrat (-1 SD on Conservrepub) high numeracy = 7 correct low numeracy = 3 correct Conserv Republican (+1 SD on Conservrepub) Low numeracy High numeracy Avg. “polarization” on crime data for low numeracy partisans 25% (± 9%) Avg. “polarization” on crime data for high numeracy partisans 46% (± 17%) crime decreases crime decreases crime increases crime increases crime increases crime increases crime increases crime decreases crime decreases crime decreases crime decreases crime decreases Gun ban crime increases crime increases crime decreases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% probabilility of correct interpretation of data probabilility of correct interpretation of data

68 Bayes Likelihood Ratio: Measuring the Weight of the Evidence
H1: MS2R = 0.15 Probability density MS2R effect The probability distributions associated with the indicated hypotheses were constructed using the standard error (0.08) of the observed effect in the experiment. . The vertical line intersecting the probability distributions is the observed effect (0.11) in KPDS.

69 Bayes Likelihood Ratio: Measuring the Weight of the Evidence
H1: MS2R = 0.15 Probability density MS2R effect The probability distributions associated with the indicated hypotheses were constructed using the standard error (0.08) of the observed effect in the experiment. . The vertical line intersecting the probability distributions is the observed effect (0.11) in KPDS.

70 Bayes Likelihood Ratio: Measuring the Weight of the Evidence
H1: MS2R = 0.15 H0: MS2R = 0.00 Probability density MS2R effect The probability distributions associated with the indicated hypotheses were constructed using the standard error (0.08) of the observed effect in the experiment. . The vertical line intersecting the probability distributions is the observed effect (0.11) in KPDS.

71 Bayes Likelihood Ratio: Measuring the Weight of the Evidence
observed effect ( 0.20) is roughly 13x more consistent with Hypothesis 1 (0.15) than with Hypothesis 0 (“null” hypothesis) H1: MS2R = 0.15 observed effect ( 0.20) H0: MS2R = 0.00 Probability density MS2R effect The probability distributions associated with the indicated hypotheses were constructed using the standard error (0.08) of the observed effect in the experiment. . The vertical line intersecting the probability distributions is the observed effect (0.11) in KPDS.

72 Bayes Likelihood Ratio: Measuring the Weight of the Evidence
observed effect ( 0.11) is roughly 30x more consistent with Hypothesis 1 (0.15) than with Hypothesis 0 (“null” hypothesis) H0: MS2R = 0.00 H1: MS2R = 0.15 Probability density MS2R effect The probability distributions associated with the indicated hypotheses were constructed using the standard error (0.04) of the observed effect in the experiment. The vertical line intersecting the probability distributions is the observed effect (0.11) in the “Guns first” condition.

73 Weight of the evidence Probability density
observed effect ( 0.20) is roughly 400x more consistent with Hypothesis 1 than with Hypothesis 3 (-0.10) H1: MS2R = 0.15 H0: MS2R = 0.00 observed effect ( 0.20) H2: MS2R -0.10 Probability density MS2R effect The probability distributions associated with the indicated hypotheses were constructed using the standard error (0.08) of the observed effect in the experiment. The vertical line intersecting the probability distributions is the observed effect (0.11) in KPDS.

74 Weight of the evidence Probability density
observed effect ( 0.02) is roughly 900x more consistent with Hypothesis 0 (“null effect”) than with Hypothesis 2 (-0.10) H0: MS2R = 0.00 H2: MS2R = -0.10 H0: MS2R = 0.00 H1: MS2R = 0.15 H1: MS2R = 0.15 Probability density MS2R effect The probability distributions associated with the indicated hypotheses were constructed using the standard error (0.04) of the observed effect in the experiment. The vertical line intersecting the probability distributions is the observed effect (0.02) in the “Guns 2nd” condition.

75 Prior odds X Likelihood ratio = Posterior odds

76

77

78

79 Odds & ends first, scatter plots for huge samples—it can work sometimes

80 Between low and moderate
“How much risk do you believe global warming pose to human health, safety, or prosperity?” Extremely high risk High Between moderate and high Moderate Between low and moderate Low Very low None at all > avg Left_Right < avg Left_Right N = Data source: CCP/Annenberg Public Policy Cntr, Jan. 5-19, 2016.

81

82 The Science Communication Problem
Data source: CCP/Annenberg Public Policy Cntr, Jan. 5-19, 2016.

83 The Science Communication Problem
Data source: CCP/Annenberg Public Policy Cntr, Jan. 5-19, 2016.

84 The Science Communication Problem
r = -0.65, p < 0.01 Data source: CCP/Annenberg Public Policy Cntr, Jan. 5-19, 2016.

85

86 Odds & ends next: on “spikey” 0.95s vs. CI bands

87

88

89

90 Overlapping PDDs for identifying and understanding the import of individual differences

91 Science Curiosity Scale
high OSI lib Dem male low relig white Science Curiosity Scale non-white female high relig con Repub low OSI

92

93 above avg. religiosity below avg. religiosity
Someone who is below average in religiosity is about 1.2x more likely to score at or above the 90th percentile on SCS than is someone who Is above average in religiosity. Is this a practically relevant difference? YOU decide. Science Curiosity Scale

94 black white 1.7x Science Curiosity Scale

95 female male 2x Science Curiosity Scale

96 above avg. left_right below avg left_right 1.8x Science Curiosity Scale

97 Science Curiosity Scale
high OSI lib Dem male low relig white Science Curiosity Scale non-white female high relig con Repub low OSI

98 Now some pious “dos” and “don’ts

99 Don’t Jump into statistical significance testing and the like without first graphically reporting raw data in a manner shows the data in fact corroborate and warrant the subsequently selected statistical model. Confine reporting to textual representations of statistical significance. Use ANOVA to generate an omnibus F-statistic and thereafter split the data to assess significance of interactions. Treat “significance” of variables in a regression analysis as sufficient to support claims that necessarily depend on the nature, including effect size and location of differences in the relevant measure; graph the regression ouput and then see what inferences make sense. Use bar graphs (with some exceptions) to report either raw or refined data.

100 Don’t Jump into statistical significance testing and the like without first graphically reporting raw data in a manner shows the data in fact corroborate and warrant the subsequently selected statistical model. Confine reporting to textual representations of statistical significance. Use ANOVA to generate an omnibus F-statistic and thereafter split the data to assess significance of interactions. Treat “significance” of variables in a regression analysis as sufficient to support claims that necessarily depend on the nature, including effect size and location of differences in the relevant measure; graph the regression ouput and then see what inferences make sense. Use bar graphs (with some exceptions) to report either raw or refined data.

101 Don’t Jump into statistical significance testing and the like without first graphically reporting raw data in a manner shows the data in fact corroborate and warrant the subsequently selected statistical model. Confine reporting to textual representations of statistical significance. Use ANOVA to generate an omnibus F-statistic and thereafter split the data to assess significance of interactions. Treat “significance” of variables in a regression analysis as sufficient to support claims that necessarily depend on the nature, including effect size and location of differences in the relevant measure; graph the regression ouput and then see what inferences make sense. Use bar graphs (with some exceptions) to report either raw or refined data.

102 Don’t Jump into statistical significance testing and the like without first graphically reporting raw data in a manner shows the data in fact corroborate and warrant the subsequently selected statistical model. Confine reporting to textual representations of statistical significance. Use ANOVA to generate an omnibus F-statistic and thereafter split the data to assess significance of interactions. Treat “significance” of variables in a regression analysis as sufficient to support claims that necessarily depend on the nature, including effect size and location of differences in the relevant measure; graph the regression ouput and then see what inferences make sense. Use bar graphs (with some exceptions) to report either raw or refined data.

103 Don’t Jump into statistical significance testing and the like without first graphically reporting raw data in a manner shows the data in fact corroborate and warrant the subsequently selected statistical model. Confine reporting to textual representations of statistical significance. Use ANOVA to generate an omnibus F-statistic and thereafter split the data to assess significance of interactions. Treat “significance” of variables in a regression analysis as sufficient to support claims that necessarily depend on the nature, including effect size and location of differences in the relevant measure; graph the regression ouput and then see what inferences make sense. Use bar graphs (with some exceptions) to report either raw or refined data.

104 Don’t Jump into statistical significance testing and the like without first graphically reporting raw data in a manner shows the data in fact corroborate and warrant the subsequently selected statistical model. Confine reporting to textual representations of statistical significance. Use ANOVA to generate an omnibus F-statistic and thereafter split the data to assess significance of interactions. Treat “significance” of variables in a regression analysis as sufficient to support claims that necessarily depend on the nature of the observations, including effect size and location of differences in the relevant measure; graph the regression ouput and then see what inferences make sense. Use bar graphs (with some exceptions) to report either raw or refined data.

105

106 Don’t Jump into statistical significance testing and the like without first graphically reporting raw data in a manner shows the data in fact corroborate and warrant the subsequently selected statistical model. Confine reporting to textual representations of statistical significance. Use ANOVA to generate an omnibus F-statistic and thereafter split the data to assess significance of interactions. Treat “significance” of variables in a regression analysis as sufficient to support claims that necessarily depend on the nature of the observations, including effect size and location of differences in the relevant measure; graph the regression ouput and then see what inferences make sense. Use bar graphs (with some exceptions) to report either raw or refined data.

107 Don’t Jump into statistical significance testing and the like without first graphically reporting raw data in a manner shows the data in fact corroborate and warrant the subsequently selected statistical model. Confine reporting to textual representations of statistical significance. Use ANOVA to generate an omnibus F-statistic and thereafter split the data to assess significance of interactions. Treat “significance” of variables in a regression analysis as sufficient to support claims that necessarily depend on the nature of the observations, including effect size and location of differences in the relevant measure; graph the regression ouput and then see what inferences make sense. Use bar graphs (with some exceptions) to report either raw or refined data.

108 Do Graphically report the raw data before doing anything else.
Use multivariate regression models (linear or nonlinear as appropriate) to test for significance and effect sizes. Use graphic reporting both to enable visual testing of key study hypotheses and to convey the information contained in the multivariate regression model. Consider using Monte Carlo simulations, both to generate key statistical measures (point estimates, SE’s etc.) and to generate data that can be graphically reported. Consider constructing and reporting Bayesian LRs or Bayes Factors to convey information—visually!—on weight of the evidence.

109 Do Graphically report the raw data before doing anything else.
Use multivariate regression models (linear or nonlinear as appropriate) to test for significance and effect sizes. Use graphic reporting both to enable visual testing of key study hypotheses and to convey the information contained in the multivariate regression model. Consider using Monte Carlo simulations, both to generate key statistical measures (point estimates, SE’s etc.) and to generate data that can be graphically reported. Consider constructing and reporting Bayesian LRs or Bayes Factors to convey information—visually!—on weight of the evidence.

110 Do Graphically report the raw data before doing anything else.
Use multivariate regression models (linear or nonlinear as appropriate) to test for significance and effect sizes. Use graphic reporting both to enable visual testing of key study hypotheses and to convey the information contained in the multivariate regression model. Consider using Monte Carlo simulations, both to generate key statistical measures (point estimates, SE’s etc.) and to generate data that can be graphically reported. Consider constructing and reporting Bayesian LRs or Bayes Factors to convey information—visually!—on weight of the evidence.

111 Do Graphically report the raw data before doing anything else.
Use multivariate regression models (linear or nonlinear as appropriate) to test for significance and effect sizes. Use graphic reporting both to enable visual testing of key study hypotheses and to convey the information contained in the multivariate regression model. Consider using Monte Carlo simulations, both to generate key statistical measures (point estimates, SE’s etc.) and to generate data that can be graphically reported. Consider constructing and reporting Bayesian LRs or Bayes Factors to convey information—visually!—on weight of the evidence.

112 Do Graphically report the raw data before doing anything else.
Use multivariate regression models (linear or nonlinear as appropriate) to test for significance and effect sizes. Use graphic reporting both to enable visual testing of key study hypotheses and to convey the information contained in the multivariate regression model. Consider using Monte Carlo simulations, both to generate key statistical measures (point estimates, SE’s etc.) and to generate data that can be graphically reported. Consider constructing and reporting Bayesian LRs or Bayes Factors to convey information—visually!—on weight of the evidence.

113 Do Graphically report the raw data before doing anything else.
Use multivariate regression models (linear or nonlinear as appropriate) to test for significance and effect sizes. Use graphic reporting both to enable visual testing of key study hypotheses and to convey the information contained in the multivariate regression model. Consider using Monte Carlo simulations, both to generate key statistical measures (point estimates, SE’s etc.) and to generate data that can be graphically reported. Consider constructing and reporting Bayesian LRs or Bayes Factors to convey information—visually!—on weight of the evidence.

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