Statistical Inference as Severe Testing

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Statistical Inference as Severe Testing Page 70

by Deborah G Mayo


  Strassler, M., 212

  Strawderman, W., 391

  Student’ s t, 141 , 345 , 378

  suddenly smitten, 131 , 137 , 388

  sufficient statistic, 147 – 148 , 200 , 298 , 385 – 386 , 303n1 , 383 complete, 385 – 386

  Sugden, R., 21

  Suppes, P., 86

  tacking paradox, see irrelevant conjunctions, paradox of

  Talbott, W., 416

  Taleb, N., 78 , 267

  Tentori, K., 72

  testing, proving (in the biblical sense), 302

  Texas marksman (sharpshooter), 19 , 31 , 158 , 276

  Thaler, R., 101

  thalidomide, 282

  Thalos, M., 415

  The Significance Test Controversy (Morrison and Henkel), 239 , 274 , 280

  Tonelli, G., 210

  tradeoffs and benchmarks, 328 – 329

  Translation Guide (severe tester’ s), 52 , 170 , 204 , 335

  transparency, 3 , 237

  TSA screening, 363

  Tuerlinckx, F., 105

  Tversky, A., 422

  two measuring instruments of different precisions (Cox 1958), 170 – 172 , 180 , 199 ; see also weak conditionality principle

  Type 1, Type 2 error probabilities, 9 , 137 – 140 ; see also error probabilities

  Type II rationality, 418

  underdetermination, 376 weak severity blocks, 108

  unifications (reconciliations), see Bayesian vs. frequentist

  uniformity of nature, 62

  uniformly most powerful (UMP) test, 35 , 136 , 139 , 139n4 , 141 , 262 UMP unbiased, examples, 141 – 142

  and uniformly most accurate CIs, 191

  Fisher on, 386 , 390

  uninformative priors, do not exist, 231 , 400

  universal Bayesianism, 417

  unobservables, 297

  Urbach, P., 48 , 240 , 242 , 289 , 379

  van Belle, G., 364

  Vanpaemel, W, 105

  Vasudevan, A., 419

  Venn, J., 387

  Vigen, T., 317

  von Mises, R., 113

  Wagenmakers, E.-J., 253 , 269 , 283 , 284

  Wald, A., 139 – 140 , 146 – 147 , 386 , 390

  Waller, N., 95

  Wang, J., 6 , 18

  Wasserman, L., 24 , 204 , 287 , 289 , 392 , 400 , 411n6 , 429 , 433

  Wasserstein, R., 17 , 215 – 216 , 395

  warranted inference see inductive inference

  water plant accident, 142 – 145 , 153 , 186 , 249 – 251 , 259 , 306 – 307 , 348 , 353 – 354 , 359 – 360

  weak conditionality principle (WCP), 172 – 173 claim it leads to LP, 172 – 173 ; see also Likelihood Principle

  Weak Law of Large Numbers, 408

  weak repeated sampling principle, 45 , 303 , 396

  Wellek, S., 162n11

  Wertheimer, N., 158

  Westfall, P., 279 , 281n7

  Wilks, S., 134 , 139

  Will, C., 124 , 160 – 161

  Williamson, J., 416 – 417

  Wilson, R., 21

  Wolpert, R., 49 , 199 , 430 – 431

  Woodward, J., 121 , 235

  Worrall, J., 91 , 92n3 , 290

  Wright, F., 59

  Xie, M., 391

  Xu, F., 79

  Yates, F., 390

  Young, S., 279 , 281n7 , 293

  Zabell, S., 390

  Zidek, J., 413

  Ziliak, S., 229 – 230 , 272n2 , 330 – 331

 

 

 


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