# References
Baker, Monya. 2017\. “Reproducibility: Check Your Chemistry.” _Nature_ 548 (7668): 485–88\. doi:[10.1038/548485a](https://doi.org/10.1038/548485a).
Bem, Daryl J. 2011\. “Feeling the Future: Experimental Evidence for Anomalous Retroactive Influences on Cognition and Affect.” _J Pers Soc Psychol_ 100 (3): 407–25\. doi:[10.1037/a0021524](https://doi.org/10.1037/a0021524).
Breiman, Leo. 2001\. “Statistical Modeling: The Two Cultures (with Comments and a Rejoinder by the Author).” _Statist. Sci._ 16 (3). The Institute of Mathematical Statistics: 199–231\. doi:[10.1214/ss/1009213726](https://doi.org/10.1214/ss/1009213726).
Camerer, Colin F., Anna Dreber, Felix Holzmeister, Teck-Hua Ho, Jürgen Huber, Magnus Johannesson, Michael Kirchler, et al. 2018\. “Evaluating the Replicability of Social Science Experiments in Nature and Science Between 2010 and 2015.” _Nature Human Behaviour_ 2: 637–44.
Christensen, Garret S, and Edward Miguel. 2016\. “Transparency, Reproducibility, and the Credibility of Economics Research.” Working Paper 22989\. Working Paper Series. National Bureau of Economic Research. doi:[10.3386/w22989](https://doi.org/10.3386/w22989).
Copas, J. B. 1983\. “Regression, Prediction and Shrinkage (with Discussion).” _Journal of the Royal Statistical Society, Series B: Methodological_ 45: 311–54.
Darley, John M, Mark P Zanna, and Henry L Roediger. 2004\. _The Compleat Academic: A Career Guide_. 2nd ed. Washington, DC: American Psychological Association. [http://www.loc.gov/catdir/toc/fy037/2003041830.html](http://www.loc.gov/catdir/toc/fy037/2003041830.html).
Dehghan, Mahshid, Andrew Mente, Xiaohe Zhang, Sumathi Swaminathan, Wei Li, Viswanathan Mohan, Romaina Iqbal, et al. 2017\. “Associations of Fats and Carbohydrate Intake with Cardiovascular Disease and Mortality in 18 Countries from Five Continents (Pure): A Prospective Cohort Study.” _Lancet_ 390 (10107): 2050–62\. doi:[10.1016/S0140-6736(17)32252-3](https://doi.org/10.1016/S0140-6736(17)32252-3).
Efron, Bradley. 1998\. “R. a. Fisher in the 21st Century (Invited Paper Presented at the 1996 R. a. Fisher Lecture).” _Statist. Sci._ 13 (2). The Institute of Mathematical Statistics: 95–122\. doi:[10.1214/ss/1028905930](https://doi.org/10.1214/ss/1028905930).
Errington, Timothy M, Elizabeth Iorns, William Gunn, Fraser Elisabeth Tan, Joelle Lomax, and Brian A Nosek. 2014\. “An Open Investigation of the Reproducibility of Cancer Biology Research.” _Elife_ 3 (December). doi:[10.7554/eLife.04333](https://doi.org/10.7554/eLife.04333).
Fisher, R.A. 1925\. _Statistical Methods for Research Workers_. Edinburgh Oliver & Boyd.
Galak, Jeff, Robyn A LeBoeuf, Leif D Nelson, and Joseph P Simmons. 2012\. “Correcting the Past: Failures to Replicate Psi.” _J Pers Soc Psychol_ 103 (6): 933–48\. doi:[10.1037/a0029709](https://doi.org/10.1037/a0029709).
Gardner, Christopher D, Alexandre Kiazand, Sofiya Alhassan, Soowon Kim, Randall S Stafford, Raymond R Balise, Helena C Kraemer, and Abby C King. 2007\. “Comparison of the Atkins, Zone, Ornish, and Learn Diets for Change in Weight and Related Risk Factors Among Overweight Premenopausal Women: The a to Z Weight Loss Study: A Randomized Trial.” _JAMA_ 297 (9): 969–77\. doi:[10.1001/jama.297.9.969](https://doi.org/10.1001/jama.297.9.969).
Ioannidis, John P A. 2005\. “Why Most Published Research Findings Are False.” _PLoS Med_ 2 (8): e124\. doi:[10.1371/journal.pmed.0020124](https://doi.org/10.1371/journal.pmed.0020124).
Kaplan, Robert M, and Veronica L Irvin. 2015\. “Likelihood of Null Effects of Large Nhlbi Clinical Trials Has Increased over Time.” _PLoS One_ 10 (8): e0132382\. doi:[10.1371/journal.pone.0132382](https://doi.org/10.1371/journal.pone.0132382).
Kass, Robert E., and Adrian E. Raftery. 1995\. “Bayes Factors.” _Journal of the American Statistical Association_ 90 (430). Taylor & Francis: 773–95\. doi:[10.1080/01621459.1995.10476572](https://doi.org/10.1080/01621459.1995.10476572).
Kerr, N L. 1998\. “HARKing: Hypothesizing After the Results Are Known.” _Pers Soc Psychol Rev_ 2 (3): 196–217\. doi:[10.1207/s15327957pspr0203_4](https://doi.org/10.1207/s15327957pspr0203_4).
Neyman, J. 1937\. “Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability.” _Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences_ 236 (767). The Royal Society: 333–80\. doi:[10.1098/rsta.1937.0005](https://doi.org/10.1098/rsta.1937.0005).
Neyman, J., and K. Pearson. 1933\. “On the Problem of the Most Efficient Tests of Statistical Hypotheses.” _Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences_ 231 (694-706). The Royal Society: 289–337\. doi:[10.1098/rsta.1933.0009](https://doi.org/10.1098/rsta.1933.0009).
Open Science Collaboration. 2015\. “PSYCHOLOGY. Estimating the Reproducibility of Psychological Science.” _Science_ 349 (6251): aac4716\. doi:[10.1126/science.aac4716](https://doi.org/10.1126/science.aac4716).
Pesch, Beate, Benjamin Kendzia, Per Gustavsson, Karl-Heinz J?ckel, Georg Johnen, Hermann Pohlabeln, Ann Olsson, et al. 2012\. “Cigarette Smoking and Lung Cancer–relative Risk Estimates for the Major Histological Types from a Pooled Analysis of Case-Control Studies.” _Int J Cancer_ 131 (5): 1210–9\. doi:[10.1002/ijc.27339](https://doi.org/10.1002/ijc.27339).
Schenker, Nathaniel, and Jane F. Gentleman. 2001\. “On Judging the Significance of Differences by Examining the Overlap Between Confidence Intervals.” _The American Statistician_ 55 (3). [American Statistical Association, Taylor & Francis, Ltd.]: 182–86\. [http://www.jstor.org/stable/2685796](http://www.jstor.org/stable/2685796).
Simmons, Joseph P, Leif D Nelson, and Uri Simonsohn. 2011\. “False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant.” _Psychol Sci_ 22 (11): 1359–66\. doi:[10.1177/0956797611417632](https://doi.org/10.1177/0956797611417632).
Smaldino, Paul E, and Richard McElreath. 2016\. “The Natural Selection of Bad Science.” _R Soc Open Sci_ 3 (9): 160384\. doi:[10.1098/rsos.160384](https://doi.org/10.1098/rsos.160384).
Stigler, Stephen M. 2016\. _The Seven Pillars of Statistical Wisdom_. Harvard University Press.
Teicholz, Nina. 2014\. _The Big Fat Surprise_. Simon & Schuster.
Wakefield, A J. 1999\. “MMR Vaccination and Autism.” _Lancet_ 354 (9182): 949–50\. doi:[10.1016/S0140-6736(05)75696-8](https://doi.org/10.1016/S0140-6736(05)75696-8).
Wansink, Brian, David R Just, and Collin R Payne. 2012\. “Can Branding Improve School Lunches?” _Arch Pediatr Adolesc Med_ 166 (10): 1–2\. doi:[10.1001/archpediatrics.2012.999](https://doi.org/10.1001/archpediatrics.2012.999).
- 前言
- 0.1 本書為什么存在?
- 0.2 你不是統計學家-我們為什么要聽你的?
- 0.3 為什么是 R?
- 0.4 數據的黃金時代
- 0.5 開源書籍
- 0.6 確認
- 1 引言
- 1.1 什么是統計思維?
- 1.2 統計數據能為我們做什么?
- 1.3 統計學的基本概念
- 1.4 因果關系與統計
- 1.5 閱讀建議
- 2 處理數據
- 2.1 什么是數據?
- 2.2 測量尺度
- 2.3 什么是良好的測量?
- 2.4 閱讀建議
- 3 概率
- 3.1 什么是概率?
- 3.2 我們如何確定概率?
- 3.3 概率分布
- 3.4 條件概率
- 3.5 根據數據計算條件概率
- 3.6 獨立性
- 3.7 逆轉條件概率:貝葉斯規則
- 3.8 數據學習
- 3.9 優勢比
- 3.10 概率是什么意思?
- 3.11 閱讀建議
- 4 匯總數據
- 4.1 為什么要總結數據?
- 4.2 使用表格匯總數據
- 4.3 分布的理想化表示
- 4.4 閱讀建議
- 5 將模型擬合到數據
- 5.1 什么是模型?
- 5.2 統計建模:示例
- 5.3 什么使模型“良好”?
- 5.4 模型是否太好?
- 5.5 最簡單的模型:平均值
- 5.6 模式
- 5.7 變異性:平均值與數據的擬合程度如何?
- 5.8 使用模擬了解統計數據
- 5.9 Z 分數
- 6 數據可視化
- 6.1 數據可視化如何拯救生命
- 6.2 繪圖解剖
- 6.3 使用 ggplot 在 R 中繪制
- 6.4 良好可視化原則
- 6.5 最大化數據/墨水比
- 6.6 避免圖表垃圾
- 6.7 避免數據失真
- 6.8 謊言因素
- 6.9 記住人的局限性
- 6.10 其他因素的修正
- 6.11 建議閱讀和視頻
- 7 取樣
- 7.1 我們如何取樣?
- 7.2 采樣誤差
- 7.3 平均值的標準誤差
- 7.4 中心極限定理
- 7.5 置信區間
- 7.6 閱讀建議
- 8 重新采樣和模擬
- 8.1 蒙特卡羅模擬
- 8.2 統計的隨機性
- 8.3 生成隨機數
- 8.4 使用蒙特卡羅模擬
- 8.5 使用模擬統計:引導程序
- 8.6 閱讀建議
- 9 假設檢驗
- 9.1 無效假設統計檢驗(NHST)
- 9.2 無效假設統計檢驗:一個例子
- 9.3 無效假設檢驗過程
- 9.4 現代環境下的 NHST:多重測試
- 9.5 閱讀建議
- 10 置信區間、效應大小和統計功率
- 10.1 置信區間
- 10.2 效果大小
- 10.3 統計能力
- 10.4 閱讀建議
- 11 貝葉斯統計
- 11.1 生成模型
- 11.2 貝葉斯定理與逆推理
- 11.3 進行貝葉斯估計
- 11.4 估計后驗分布
- 11.5 選擇優先權
- 11.6 貝葉斯假設檢驗
- 11.7 閱讀建議
- 12 分類關系建模
- 12.1 示例:糖果顏色
- 12.2 皮爾遜卡方檢驗
- 12.3 應急表及雙向試驗
- 12.4 標準化殘差
- 12.5 優勢比
- 12.6 貝葉斯系數
- 12.7 超出 2 x 2 表的分類分析
- 12.8 注意辛普森悖論
- 13 建模持續關系
- 13.1 一個例子:仇恨犯罪和收入不平等
- 13.2 收入不平等是否與仇恨犯罪有關?
- 13.3 協方差和相關性
- 13.4 相關性和因果關系
- 13.5 閱讀建議
- 14 一般線性模型
- 14.1 線性回歸
- 14.2 安裝更復雜的模型
- 14.3 變量之間的相互作用
- 14.4“預測”的真正含義是什么?
- 14.5 閱讀建議
- 15 比較方法
- 15.1 學生 T 考試
- 15.2 t 檢驗作為線性模型
- 15.3 平均差的貝葉斯因子
- 15.4 配對 t 檢驗
- 15.5 比較兩種以上的方法
- 16 統計建模過程:一個實例
- 16.1 統計建模過程
- 17 做重復性研究
- 17.1 我們認為科學應該如何運作
- 17.2 科學(有時)是如何工作的
- 17.3 科學中的再現性危機
- 17.4 有問題的研究實踐
- 17.5 進行重復性研究
- 17.6 進行重復性數據分析
- 17.7 結論:提高科學水平
- 17.8 閱讀建議
- References