FlyerTalk Forums - Boeing 737 MAX 8 crashes and effects on AA 737 MAX 8s (NOT reaccommodation)

Quote:

Originally Posted by milypan (Post 30870725)

I think that's sarcastic, but I'm not certain. I'll try to put this as simply as possible. Over the past decade, the average rate of ~100% fatal hull losses for the 737NG and A320 is less than 1 per 10,000 aircraft-years of operation (i.e., a rate of 0.0001). That includes everything -- even CFIT, terrorism, and suicide. The MAX has less than 300 aircraft-years of operation. The probability that it would suffer at least two 100% fatal hull losses in that period, if it had the same safety profile as the 737NG and A320, is less than 1 in 1,000. Hence the conclusion that there's almost certainly something wrong with it.

This got me interested enough to do some analysis of my own.

According to Boeing's own statistics (http://www.boeing.com/resources/boei...df/statsum.pdf), the 737NG has a fatal hull loss rate of 0.08 per million departures.

Under the null hypothesis, the failure rate of the MAX is the same as the failure rate of the NG (the population failure rate p = 8 x 10^{-8}. There's no reason that failures should be correlated, so the distribution of failures should have the Binomial(n, p) distribution, where n is the number of MAX departures.

If you believe Wikipedia, there have been 350 MAX deliveries, most in 2018 (https://en.wikipedia.org/wiki/List_o...and_deliveries). If we assume an average of 4 segments per day and an average of 300 days per frame that is n = 420,000 departures.

The probability of observing two or more such failures in 420000 departures (which is 1 minus the probability of observing one or none) is less than 0.0006. Since the standard for statistical significance is usually 0.05, anyone armed with these statistics would conclude that this is strong evidence the hull loss rate of the MAX is higher than the hull loss rate of the NG.

Incidentally, even if I have underestimated the number of MAX departures by a factor of 2, changing to n = 840000 the P-value is still 0.003, which is still highly significant. However, what is most interesting is that if we instead had only observed one failure until now (Lion Air), the P-value would have been just around 0.05 (depending on your assumption about n), which is much, much weaker evidence of a difference. So statistically speaking, people are actually correct to see the second event in a short period of time as cause for worry. The second failure in a short period of time

Excel calculation follows for those interested.

[img]data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAscAAADrCAYAAAC FBztKAAAgAElEQVR4nO2d3W6czJZAeSiu6lm45j36Dbh1JpYzM ZYzsWJZzglyTizLkdOxLGc0Ovp8x31eY89FQzfQVVDFT3dTvZa 0pcTGUNTmZ1FUFcHfv39l3/Gf//xn72UgCKI9OE/9D3JMEATxV4J9F+Dv37/yf//3f3svA0EQ7cF56n+QY4IgiAOR4//93//dexkIgmgPzlP/gxwTBEHsSY7/+eef2v9fX1/3XhEEQbQH56n/QY4JgiD2IMf//POP/Otf/6r97OXlZe8VQRBEe3Ce+h/kmCAIYsdyXIpxU46fn5/3XhEEQbQH56n/QY4JgiB2KMdVMW7K8XK53HtFEATRHpyn/gc5JgiC2JEcN8W4KcdPT097rwiCINqD89T/IMcEQRA7kGOdGDfl+PHxce8VQYwXy0UoQRBIuKAVyqc4ivN0uZ AwCCRoRBgtZLnvspFj4phjmcoiCrfPzzCSdN9lI7yLSeXYJMZN OX54eNh7RRBjRSpREEgYhhIEXLR8iqM4Tws5DheppGkq6SKSKA yLm7D/gnwUOSZmF2lUnINBKGEUyWKRymIRSRRFEnKfISaIyeS4TYybcn x/f7/3iiBGijRaSXEhGVF6AGUiRomjOE8Nx235NsT34/kockzMKtKoeHuzSL1/OCUOJyaR4y4xbsrxv//9771XBDFOpFEgQZTK379LWYTlv/dfLmJ4HMV5anqoS6OjkOOjyDExnyjOO7roEbuO0eXYRoybcnx3 d7f3iiDGiFWXilIgVq1toSyW+y4XMUYcxXna2nLs/7F8FDkmZhOrVmO6TRC7j1Hl2FaMm3KcZdneK4IYHiuBqF7Iiv7 HPPV7EUdxnmrkeJmW/ZD9P46PIsfETGJ1/+DtI7GPGE2OXcS4KcfN/xNzDH03ijQ6joFMxxBHcZ4aZ6s4jv6OR5FjYh6xPJ6HUuLwYhQ 5dhXj5gX49vZ27xVBDIwj76t5DHEU52lztop0IdF6+ij/u1UcRY6JeQRyTOwxBstxHzFuyvHNzc3eK4IYFua+Ybwa8yWO4j w1zrJSHMeevwU5ihwTMwnuHcT+YpAc9xXjphxfX1/vvSKIIVFcxFqDQRVzj6M4T1umIDyGwUFHkWNiJlF01fP8gZQ4z Ogtx0PEuCnHV1dXe68IYkCkkQRBKNH6VXQjFkzH40McxXl65HJ 8FDkmZhPHMr84cXjRS46HinFTjv/nf/5n7xVB9A2bp/vjeCXtexzFeWqS4+VxHMNHkWNiRlHcX4JQopTGFWJ34SzHY4hx U44vLy/3XhFEz7AcNLFqdfN/QJPPcRTn6ZEPyDuKHBPzimUqUVh0zwtDiaLF5tPufD6amCic5H gsMW7K8cXFxd4rgugX1h9HKKfIYnDFbOMozlPjVG4LST0X46PJ MTHDWEq6iCQMm+dmKKHnb3OI/YS1HI8pxk05Pj8/33tFEATRHpyn/gc5JgiCsJTjscW4Kcf//d//vfeKIAiiPThP/Q9yTBAEMfLno/vG2dnZ3stAEER7cJ76H+SYIAjiQOT49PR072UgCKI9OE/9D3JMEARxIHL8X//1X3svA0EQ7cF56n+QY4IgiL8S/PnzR15fX+Xl5UWen5/l9+/f8uvXL3l6epKfP3/K4+OjPDw8yP39vfz48UO+f/8ud3d3kmWZfPv2Tb5+/Sq3t7dyc3Mj19fX8uXLF7m6upLPnz/Lp0+f5PLyUi4uLuT8/Fw+fvwoHz58kLOzMzk9PZX379/Lu3fv5OTkhCAIgiAIgiD2HgfRcnxyciIAcNi8vb3tuwgwMeQYA ECQYwCwA3HyH3IMAIAcA4AliJP/kGMAAOQYACxBnPyHHAMAIMcAYAni5D/kGAAAOQYASxAn/yHHAADIMQBYgjj5z7HlOE+UBEEgcbbvkgDAIYEcT0EWSxAEopJ 83yUBGI1jE6dj5LhynEuiAgmCWHBjAKjST46XqURhIFE6oRzni aggkKARSsWSZAcunRPKcdnSgXjDrpmFOOWZJLGqXTvcrxm5ZJp 16Fdhuazherb+G6fz2aF8jvUxTo5d6m+EdfTNeZkTU7NxnkjcK c8THCuzXBbALxzleCnpIpKwOFF2IsdKSRzHq1Cq581kxwyW40z iIJBAJbJ9j/VRjs37C4fD4ctxcRwFSlScSJZlkiXx+uZu9+q8bE2sXHvW1x0l 9dPOYVnd9awSiXXTpUv53OtjeI5dyreffVxvJVHGMpXX2VWY5H iiY2V2ywL4h7UcLxdhcWKEEoXh7uS4eXVbt8Ac8KuwCeXYT45t f+fJocuxsf9oVwuhZh3NZfPinK7+3GVZlzKMXT6X+hiaY6c6GW Ed/XNeyN/WNWcjhSrJWrtdTHWszG1ZAB+xluM0CiSMUlku/8rfNNqfHItIFru0BO0B5NiRY9vfeXLocry6LmhatazFtBQjXct Ys3+qy7IuZRirfP3qY1iOHetkhHX0zrnpGp0notbrayvzVMfK3 JYF8JN+fY73LMf61oK89jpt/TpI10Eqi9cnfp7FotTmb1ScbQtacSHdLorhQqG98OZb2woCJXH j6lOK/1aUGzeKt+v+r/Ynz6p/s10eI5V1ZHHlddvGBsbZ33Lf6Pu2dw5djk1djuy7IrU/pNVFzGVZGUmO3bbZpz62c5xLpukGUoukvGY61sme9tG+LG0iON WxMrdlAfxklnK83XKs6R9VkSnVXEchx6roQ6XU6iKvtGImMoYc ry/WKpYkyyTLysEejQt4vvrdqn9iuWy2EUGDeLvv/2rZzf5Xlre56pXrKKRXFX3SSjkebX8rrRjbfQq5QO+SQ5fj2iv xOJEszyWrvB7uPFQ6BLb2UO6yrMW6rXDdZo/62M5x2ae3LYrrn3P59rOP9m+qWuR4qmNlbssCeMr85HgtZZsLl ql/VHWwRk2i1uswD+7QLT+o5TjPJN9ukjZcpFsu3i3iPXj/14OGLLo3GOtw3P0dox8pjMPhy7FI+ZahKm/W3Zt2Ice6AXm2r0B6SYtbfQzK8V7kWMQ559bd3pDjzmUBPOXw5 bg2W0Xl9fxWy0H7oInahbDl4ti2/LBuFTpMZXeR4zH336E/Wa9+1a77Ww6aiSXLc8lrka2OB/op74xZyPF6+q3NmyHr7kK7kGNdNLsQlW9P1pHX1+EiLY71MUs5 dtzH1ZtHmz6zyHHnsgCecvhyXIvi1bqutdckSbrRtW1ip/vdaHKcS54lklRajfQzb7jI8Zj7P7YcD91fi1e6yPHOOHg5XncX qlwj8mzT5ajrbn4I3Spq3ZUGdlvoUR+zk2PnfSyuKVZmhxx3Lg vgKYcvx51n4EzkOIvNLUc+yvEo+7v5+XbLcRFd5YTROGw5Lh+k XGcW0KzDiwF5/epjUJ/jnQ/Ic99HN7FjQB4D8uBY8UCOp+lWUdusq0wauz40Z4/wtVvFWPvLtEGHxEHLcUd/+dFmJ9ia7spmWRlJjh222bM++s5W4Vy+veyj6/XEZiq3sY+VuS0L4CceyPFm9ortOSvHHZC3NSH6+itKXXLc0jKq vQi1XJw0Utp3/6eT4/H2t3VKpjzXDPqDqThoObZqRdTLan1MavvUYLoPJdgsO9YAUvtt OtZHwVgfAXGtk3456J9zy71xb3gw7KvPywL4iBdyXHv1V5nKrH zttyVWjX593VOZVadKK/rOrgeA2MhxRWCL6ciS2pzE2xff9fy/ZdnKetBKab/9n7JbxWj7W9k3FSfraeGS2HbuWhiLw5bjyo1bKYmTbDPtn24KQ c2sN8UvKudScyDwgGU7Px9texzbb9OpPgqG59ihTkbIgcs+2nW pqLeUl1NGqq05nR331etlAfzDEzkWEckl0XwoQnvTqYhdnjQ+g mG6SeWZJErV1p3lhpHPprmIa9MNrbZlHjmdVS5GlXW1iW2P/d9ax2gD8kba32JdzY+ABKZ9g8k4dDkWEcmzRGJVPe4M54Gh1bJ Yydaxq2JDn37bZdtmq3B9yHMon3V9FIySY9c6GZgDu300fS66S Vcf6+ZDzwTHyhyXBfCMfnI8cmjleEoGf94Z4PiYgxzDMLzNcSH iXPMBwAbkGACs8FacYI2vOWaGBQBwATkGACt8FSfY4GeObT8XD QCwAjkGACv8FCeoQo4BAI5VjgHAGcTJf8gxAAByDACWIE7+Q44 BAESCP3/+yOvrq7y8vMjz87P8/v1bfv36JU9PT/Lz5095fHyUh4cHub+/lx8/fsj379/l7u5OsiyTb9++ydevX+X29lZubm7k+vpavnz5IldXV/L582f59OmTXF5eysXFhZyfn8vHjx/lw4cPcnZ2Jqenp/L+/Xt59+6dnJycyNvbG0EQBEEQBEHsNWg5BgAr3t5oVfQdcgwAQLc KALAEcfIfcgwAgBwDgCWIk/+QYwAA5BgALEGc/IccAwAgxwBgCeLkP+QYAAA5BgBLECf/IccAAMgxAFiCOPkPOQYAQI4BwBLEyX/IMQCAgxwv04VEYSBBUEYo0SKVJXIMcBQgTv5DjgEAbOU4jSQIA gnDSBZpKmmayiIKV5IcpcgxwBGAOPkPOQYAsJbjhSyW2z9Po1U Lsu53yDGAXyBO/kOOAQCG9jkuWpSjFDkG8B3EyX/IMQAAcgwAliBO/kOOAQAGyvGqW0UkKX2OAbwHcfIfcgwAMESOy0F6i+UgMUaOAeY B4uQ/5BgAoKccLxermSrCEWaqQI4B5gHi5D/kGADAWY6XsgiLOY7T4S3GyDHAfECc/IccAwC4yPFyIWEQSBAuJB04dRtyDDA/ECf/IccAANZynEpUfPBjjC/iIccA8wNx8h9yDABgKcdptGoxnkKMkWOAeYA4+Q85BgCwkuOin 3G4kLT4dPR2DOt/jBwDHD6Ik/+QYwAAFzkOWmJgqzJyDHD4IE7+Q44BAIZ+IY9uFQBHA+LkP+QY AAA5BgBLECf/IccAAMgxAFiCOPkPOQYAQI4BwBLEyX/IMQAAcgwAliBO/kOOAQCQYwCwBHHyH3IMACAS/PnzR15fX+Xl5UWen5/l9+/f8uvXL3l6epKfP3/K4+OjPDw8yP39vfz48UO+f/8ud3d3kmWZfPv2Tb5+/Sq3t7dyc3Mj19fX8uXLF7m6upLPnz/Lp0+f5PLyUi4uLuT8/Fw+fvwoHz58kLOzMzk9PZX379/Lu3fv5OTkRN7e3giCIAiCIAhir0HLMQBY8fZGq6LvkGMAALpVA IAliJP/kGMAAOQYACxBnPyHHAMAIMcAYAni5D/kGAAAOQYASxAn/yHHAADIMQBYgjj5DzkGAECOAcASxMl/yDEAAHI8HlksQRCISvJ9l2QAuWSxkiAIVhEn0r43rstb4kVd+g fi5D/kGADAVo6XqSyiUMJSgoJAgjCUKF1OJ8d5IqrYloozfekLiTL9e qd4IHR5UoiuSiTLEkk6ZNd1+bZ11OrNg7r0EcRpfuR5JrGqPMA GSuIkM56n5BgAwFaO00jCMJJFmkqappKmC4nC1cU2XAwX5C45D gIlWk9Cjkckl0QFEgSx2FWn6/KGtSDHswFxmhfrh9dAiYpjiWtvefRnLTkGABjUrWIpizCQIFzI ckI5VnG8kmSlaZVEjkekkF1dPY+yvAOzr0s/QZzmRZ4oUc1W4nWjg77BgRwDAAzsc5xGgQRBJOmULcdxJlkc6C UYOR4R5BjaQZx8oHzjo79ukmMAgDFajqN02m4Vcbb5d1PEdHJs FGZDN4DK8nkW17pyxFm+Lkusqn32NNJWEbo8i0VVllexqY/fakDbZpuBKBVL1ly4UsbNADglSedDQS5ZEtfWH6jKfq1XX/l9JcxdvbuWb9aBXb3Z/KxPXs11Zln/mnpUKhnUnWSOIE4+UJ4vtBwDAJjoJ8fLpSyiUIIglMVymBhbyb FshKxTmHpKVFAMWmn2zVPlv1UscbwRpK1WzXI9FcmqLr/dx29zk1JxIlmWVQSsceOqrbvoP6i65HjTQhQoJXGxX9pBjnkmW VY8AKhYkiyTLMskNzXcdiy/7ke8/t3m4cJUb1PJsbnO7Ot/fexVl1OGfvAegzj5QCZxYB4rQI4BAFzkOI1q8hdGw/sau8ix9qI+phw3hGgzmKUhaR2t2NstMmW59evfdubmfret28y6/Nt9UbTlGbVbRa4T62K7hnqbTo71dWZf/xN2H5kZiNP8MV8XVpBjAAAXOV4ui5kqUkkXkYRhIEEQSpTuSo4 1MxuMKMdbLZo6Se2zHl2518IVS5bnktciK1pkKzLm3Ae3fSYJ7 QwRk/c5dqi3EeVYX2cu9V95Db3d3+KoQJxmznownnmGGXIMADDKgLzh XSts5Xir1XNKOS63NYIcb/+u3I+WGCTHhlbaxvrq+za2HOeSZ4kkcVx06Yj1N+a9yLFj/Vf7nKv2eWJ9BnGaM6Y3RnXIMQDA0C/kLRcSBsPnOraXY6mL3dzlWCWalssibNetZc9ynDUGAdbigOTYt v7LLWX1gZn0OYZ50D5DRRVyDAAwkhwP7VrhJMfV19yJWY63pWh/crzdv9XxAxqz6laxyU99VoxxulUMz2vL31iSax8u/AdxmiP2YixCjgEARGbXraKgMTOETo6bf7cZYDetHLsOyNPKW57 XB7T1mPdXO7uHiEg+9YA8U6t10Zd3oBwPz2v976zqfwvTseE3i NPcqIqx3VlNjgEArOR4KYswlChabD4fvdjh56MNAlKda7e+SHU Ks6Kvazll1+Qtx2o9t29zKjfj+oPVFGHllGdJPNbnlCv9aitTu Wln4Kju1wjdKppTnyW1OYJ7yvFoed2un9b6zxNR1eneKl0rjsy NEaeZsb5Gqk2//1po5oIkxwAAli3Hy/XsFJsIw0jSEeY47ivHm5HXuum4MklURQSLDzusbhbTynGSi+RJ 42MiRkHb/gjFat5gi7mArcgl0XzkQj/rwpgD8vK1ZK7rIMvt6984g8gYea2Xs7v+LXN0BCBOc6LyMGkKz blLjgEAhvY5Him0cgwABwXi5D/kGAAAOQYASxAn/yHHAADIMQBYgjj5DzkGAECOAcASxMl/yDEAAHIMAJYgTv5DjgEAkGMAsARx8h9yDAAgEvz580deX1/l5eVFnp+f5ffv3/Lr1y95enqSnz9/yuPjozw8PMj9/b38+PFDvn//Lnd3d5JlmXz79k2+fv0qt7e3cnNzI9fX1/Llyxe5urqSz58/y6dPn+Ty8lIuLi7k/PxcPn78KB8+fJCzszM5PT2V9+/fy7t37+Tk5ETe3t4IgiAIgiAIYq9ByzEAWPH2Rqui75BjAAC6V QCAJYiT/5BjAADkGAAsQZz8hxwDACDHAGAJ4uQ/5BgAADkGAEsQJ/8hxwAAyDEAWII4+Q85BgBAjgHAEsTJf8gxAAByPB5ZLEEQiEry fZdkALlksZIgCFYRJzLa3nhRP8cN4uQ/5BgAYIAcLxehBEEg4WI5jRzniahC0lSc6UtfCJfp1zvFA/nLk0KMVSJZlkiCHE9GWddzqo+jEKcs3lx3NLnJ80xiVXmADJTE SWY4T1YPm2q9bCBKxZJpFz6EZY8kxwAAHfSU41Si4kK7CzkOAi Vah0CORySXRAUSBLFMUp2zr58+ZBIXDxvNvUaOD5HyHNDL8frh MVCi4lji2luW5llTWZdSEsdxRaqb17NDWHaF/zkGAOimlxyn0eYGMrUcq7hoydEIBnI8JsWNVFfPYzD7+umDWY7 niO/itJJfJUmiP1bzRIlqthKvH+LrsrkW6cbFKS/Og+rPD2HZEt9zDABgg7scp5EEQSiLdCHhLlqO40yyONBLMHI8I sjx+CDH86HIVZw5Hqub1tnNdaj8ma51tvmG5hCW3eB3jgEA7HC U41V3inCxlL/L3cnx+t9NydDJsVGYDTeEyvJ5pb9hECiJy455eSKxqvxcd9Os3 FDzLBZVWV7FA/skVsq4GTCnJOl8KMglS+La+gNV2a/16iu/r0T3Q4fd+vvVz/a6lUo0XT6G16Hx4atR7nJ79fLrjwlTna5b64wC5l6n2mNXK3a2 darHZ3Fa5au4NvSS46qEtj8UrbZVLn8Iy27wOccAALY4yPFSFm EgQbiQ5d+/u5Vj2chG7YY1ohwHRV+8Zl9CVf5bxRLHLYN1yvVUBK26vLlPoh IVJ5JlWUVcGjet2rqL/o6qS441/Q0rElkb5JhnkmXFA4CKJckyybJM8lY3cFh/j/pZ57taN8rUp3JgHTaOte1ybNa17iu8rqfNg1PtmCjqVAX1Ol07 rlbAetapUpU6VcZj1K5OzXgrTmUXrrIinOS4kNDqdaXleFr9Wm 2uUYewbAVvcwwA4IC1HK/6GUeSlj/bsRxrb0JjyrGpz+CW9LS3Ym+3xpTl1q9/25k1NzXjus2Y+huayuParcJ5/U71Y1eW8erQ9JpZ0/qW6x4aTK10La13GgEbrU61x+jwbjN+ipOmXhzkWJuzQxBe5BgA oDdWcryati2UxbLy853LsWaE/4hyvHUjNN5cHNejK/f6hhxLlueS1yIrWnD73axby2gsT7VMNvLUY/196idQkpjmnBq5DrWyYF3vDnJtXPeYdapbl02dtuOlOLVcQzrz vh6M18jZIQgvcgwA0JtuOU4jCYJAorTx8z3I8VYL2pRyXB2gM2 g9ut+V+9ESg+S4YyCYdrS6ixz3WL9T/Ui9n7fSzSU7ch1qjjdTv8xVv+NEkjguuj7EeklykuMx69RwjHb WaTv+iZPhHLc630xvYOQwhBc5BgDoTYccF/2MuyREJ8+TyLHUJWHucqwSTatnEbbr1uKBHJelyuqDIXWDnsap w+bAKrM4KeN5cOByXP7WWKft+CZO1f7jceVBZz0XcPHz7b79uh kqqhzCIDsG5AEA9KWz5XiZppLqYhGtWo6jhaRpKsvljuS4KjGJ yyvR/cnxdiuN4wc3jqRbRdvsGNtzs05Qh9VltA9Zm2OvPnvEHLpVaNb ZMt+tDt/EqTquoC1MgybN1dZW/82xE4ew7AbfcgwA0Ifen4/eT7eKgsbMBzo53pr4fn0jnFaOXQfkaWUtz+uDvnrMEayd3UNEJ B9nQJ7z+h3rR7PFrXyMX4cbmU1inTy0DLzTikiLoGjK07dO+8q x+RjXczTi1FmvgX5qveqShmNTN4DvEJYtOZocAwC0ME85lo1Im Fv3Nq9FVTnd1+Qtx2o9/21zqjLj+oPV1FrltGBJ7NbqaqbSJ7cyLZi+NayyX9azGTiu36V +8kRUdXq2SjeAejrGr8Pa/MTGqd0206EltXmDt2V0vb5ymrXWeY771KmlHFvXqZmjESdDvdZ zqYla/4tqLsvuGh3db/a67IqjyTEAQAuzlePNSHHdVF6ZJKoiFcVHITJda+DIcpzkInli 80GG1bqaH7BYzYlrK0Fd5JJoPpChn62gz1RfDut3qh/LenFZ1rYOO6fNy9fyvS5/luuPrdUKK0JiM5eua53az1ZhX6d6jkacuuagNkXz3MmzrWNFxY bz6xCWlSPKMQBAC/3leMTQyjEAHBSIk/+QYwAA5BgALEGc/IccAwAgxwBgCeLkP+QYAAA5BgBLECf/IccAAMgxAFiCOPkPOQYAQI4BwBLEyX/IMQCASPDnzx95fX2Vl5cXeX5+lt+/f8uvX7/k6elJfv78KY+Pj/Lw8CD39/fy48cP+f79u9zd3UmWZfLt2zf5+vWr3N7eys3NjVxfX8uXL1/k6upKPn/+LJ8+fZLLy0u5uLiQ8/Nz+fjxo3z48EHOzs7k9PRU3r9/L+/evZOTkxN5e3sjCIIgCIIgiL0GLccAYMXbG62KvkOOAQDoVgEAl iBO/kOOAQCQYwCwBHHyH3IMAIAcA4AliJP/kGMAAOQYACxBnPyHHAMAIMcAYAni5D/kGAAAOQYASxAn/5lbjvNESRAEEmf7LgkA+ARyvAuyWIIgEJXk+y4JQG/mJk7gzrxynEuiAgmCWHBjABgTazlOo0CCQBeRpFPJcZ5JrNRmW 0pJnM1QMJHjnVK2JlHf4zIvcRKRLBZVXDvsjoVSttpCyfaqcsl itd7W5lpVXSSp/74Rbsfq9vaUisV8abQoX8HgHJv2s+XanTev84GSOMmks0bKbVV 3pMf2azgfMwDgI5ZyvJRFGEgQRrJIU0lrsZTlFHJcvUjFscTxj C9ayPEEZBIHgQQq2bqJIsfTMC85rouurRxnyepao4vV9afZSlk ch4ESFSeSZZlkWSJxrEQlGmlTSrvuxLrps7Jf5brWYqkTd8vyF Ywmx7X9VOY8FNfGVfnqywYdfSVW53ljn123X19jj2MGAHzETY6 jdGfdKrJYf7HPEzW/ixZyPAFmOYZpmJMcr8UpGencKyWu3hxcyJROSrcKtN3K2YPywa +5nnxo+QpGk+Pmfq5bdOsPF6vreaOV2LBsY4WrfWue/47bry8y8jEDALPlQOXYs75kyPEEIMe7Zj5yXBwbcTbauad9WC+ Ey2rdo8hxm+xqrpku5SuYTI6dRN1iWVNee29//GMGAOaLpRynEgWBhIvljuXYZhRyLnkWi6r1FVQS6y5sxUUvzlY tLaq6fNkfLU8kVjbrWV1km9tWsaavnPFi69p3cHgZVtf+ymvYW v2uXitv900coQ56rV9f3pWoaKI8WNrqu8f2t46VI71pzkWOV8d HIYljiM76VX39QUz7Wr9rHYPkuP2BsCnwTuUr2M5xLpmhm8k6q i2/nXJq0+DRLcemN4t9tz/6MQMAs8ZJjmsiEoYSpePIsqnPcbdsVfqXqliSsj+d6ujfVvTRa/ZxU+W/VUcf50IMldpsu7r81oVZe7Hd3ADWfQHX4mZxQ+tZhlr/PlWVY01fxoq4K+36HLbfa/2G8uarPKsiV6u8Z5uHitb6dtx+bf+U+Zg4AmYhx83W0hFEp3wY 2z5EVkKVZEn3gLIx5LhjHc1pzZzKV7Cd47LPcltst1YbuzXYvO nRdhHRlEm3rj7bn+CYAYB5Y92tojYIbxFJGBYXxhG6Wphmq6i3 2AUridxqKebdKnEAABI/SURBVMgkNw1CaV4IK4M/qte9dT++5gXRdEE1rKc6+KX2c83F1jg/p+1NtGcZTOJt6svovj798uOtv/F3uptdS30P3r7LTd4zDl+ONf1QB4tOeXw0Wxwbg7fiZOvhXDuL gm5Anu0MPE5y7Fi+gvEG5JWD/yoP/SrWn8t5th4omFQaKIxXwLacOm9/imMGAObOoHmOy+ndonQaOV7RfBWun4Ko+TfaV2i9+6lZrkcMMy VsLV9ekGPJ8lzyWmSrm1eXfA0ug8V+9lzf9vLjrr9YwEGOx9y+ Z/3hHTh4Oa50hWn+rK/omGc+2RwHLv1/W7sDFX+7lrp15PV1OMmxZfkKJpvKbd1ibb5eVEPbEFJQ6wIxdP sTHDMAMH+GfQRkuZAwGN4X2e4jILlk64voditOniWSdE27ZLzo VQZjNNbrKsfa3239zOJV5QA5tiuDZv9N29S95nTa/sjr71rnpNtHjg8Twzk8SHRMrcYidn1YK9Ll/EZI022hpxxbla9gutkqsnVLdtd0aps3hi1T05nq0Wn7UxwzAOA DA7+QV/RFHti1wuULeVsX9UbXC2NfuGLZg5JjlWhajovorgjkGDneKYcs x9WxB7UuC6o+jsB+PmHp7PtqHBQmA+S4vUBOA/KcylcwWZ/j2roszh1D3Xd+Ltph+5McMwDgBTNqOV5Rv6hvBrXV++25Su14c qy9eDu+5rdicBlqfzFJt4rN9ulW4QNzkOOusG8N7J4xwSxqmr8 ddSo33bG3LZ5O5SvoO1vFZvGR5bh2flucez3keLxjBgB8YYAcL 4s+x6EsliP3Oc4T/ZRmefPiahKkot/uxHI8xoA8/aCSXDPIsFnk4WWor85wI8jb12e7/b7rN9+YWm6Umr8db/vI8aywGGfQ+jahrXuTaRndz0f+CEhzf7QDTl3KVzDdPMe67eaS KCXJ9oV+M12jblBjWx06bd8A3SoAjh47OU4jCcNIFotytoqFRM VsFWPMfayV4/LJff3KSz+jxFp6iunQktrgvanlOKi8lmuZ5ku73c3ryupI8nK0 dueFeZQyaOogCGpTnRlbT1y333P9bfWwvoGW226d53is7SPHs8 KUx/Xx69Ynt051RoiOTzl3fj7aVsSqx3HZDcBwvXMpX8E0n4+Oa3PH b7arKV9svn53dqlw3r4B5Bjg6LGT4+ViM3VbEWEYyWLKeY7zTB LtBzI0shJXX4+tulhoRzRP0K0i35pJw7IPcLH+5kdAVvP2WlyU RytDY381da4tj+v2B6y/pRIqNz2beUrH2D5yPCv6tBw7Tden+ZCPdsrJtlkUHEWsuDZWr3 kqNpXVsnwF081WoQzbLWYjqn7ESenmYTZ8Lnrw9jUgxwBHz8AB eeOES5/jg+AQLp77LsO+tw87Z5ZyDE4cbI57fAobAKAvyHEfDkEM912Gf W8fds7BihOMxqHmuG3mDQCAsUGO+3AIYrjvMux7+7BzDlWcYDw OM8cdUzECAIwMctyHQxDDfZdh39uHnXOY4gRjQo4BAJBjALAEc fIfcgwAgBwDgCWIk/+QYwAAkeDPnz/y+voqLy8v8vz8LL9//5Zfv37J09OT/Pz5Ux4fH+Xh4UHu7+/lx48f8v37d7m7u5Msy+Tbt2/y9etXub29lZubG7m+vpYvX77I1dWVfP78WT59+iSXl5dycXEh5 +fn8vHjR/nw4YOcnZ3J6empvH//Xt69eycnJyfy9vZGEARBEARBEHsNWo4BwIq3N1oVfYccAwDQrQ IALEGc/IccAwAgxwBgCeLkP+QYAAA5BgBLECf/IccAAMgxAFiCOPkPOQYAQI4BwBLEyX/IMQAAcgwAliBO/kOOAQCQYwCwBHHyH3IMANBDjpdpJGEYSBAUEYayWCLHAL6DOPk POQYAcJLjpSwKKQ6jhaRpKmm6kEUUSpQixwC+gzj5DzkGAHCQ4 +UilCAY3kqMHAPME8TJf8gxAICtHC8XEgaBhIslfY4BjhTEyX/IMQCApRyvWo0jSScQY+QYYB4gTv5DjgEALOU4jQIJwoUsl6lEt cF40SjdLJBjgMMHcfIfcgwAYCXHxUC8KJKoOhhvsZm1ggF5AP6 DOPkPOQYAcJFjrQSnEgVFqzJyDOA1iJP/kGMAABc5NgjwGLNYIMcAhw/i5D/kGAAAOQYASxAn/yHHAAAjzFaRRsHgmSyQY4DDB3HyH3IMADB4nuOiz3GU9hZj5Bh gHiBO/kOOAQAcvpCXRmH909GLSMJgeKsxcgwwDxAn/yHHAAAOcvz3719ZpqUQBxtRZp5jgKMAcfIfcgwA4CjHUwVyDHD 4IE7+Q44BAJBjALAEcfIfcgwAgBwDgCWIk/+QYwAA5BgALEGc/IccAwAgxwBgCeLkP+QYAAA5BgBLECf/IccAACLBnz9/5PX1VV5eXuT5+Vl+//4tv379kqenJ/n586c8Pj7Kw8OD3N/fy48fP+T79+9yd3cnWZbJt2/f5OvXr3J7eys3NzdyfX0tX758kaurK/n8+bN8+vRJLi8v5eLiQs7Pz+Xjx4/y4cMHOTs7k9PTU3n//r28e/dOTk5O5O3tjSAIgiAIgiD2GrQcA4AVb2+0KvoOOQYAoFsFAFiC OPkPOQYAQI4BwBLEyX/IMQAAcgwAliBO/kOOAQCQYwCwBHHyH3IMAIAcA4AliJP/kGMAAOQYACxBnPyHHAMAIMf9yGIJgkBUku+7JAA7A3HyH3IMo1 PcL+Ns3wUB38niQIIgljEONQs5TiUKAgnaIlzIcmw5zhNRmm0p FUuS7VlKkWM4QuYvTrlksapdV5SKxe1y4rKOCZY1XBdX0bgpuC xbMGqO80Ri1fNmZSq7UhLrKrBcvmlglfUok521ylsueRaLUg73 oWrZlc2+Z0U9OdRVS/2oODEf06Z6EhHJM4mVMtR1LokyHUuNuqndF4u/U4nUiuSaXxHJm+ULlMRJJr3vwn2Ozx7lrpHFm+PRxh+GbM9p/3rkd6xztLaI2j4XW69jjXoslh3DzSzkeCnLNJVUF4tIwiCQcLE cv+W4rBClJI7jVVROjL2KKXIMR8i85bhy8S+vKevriRK7U9llH RMtW178Vby5Lq4j0wqI1bIFY+W4vMk5CV99BdvX/7jl+m8hx8Y8G+W4Kq1KlOY+FOjqsXEz77pP9Korbf3E3ds11VN V2hrrKteTJY1jSG0eFKo/TzRiY8yXbX6LHG3yUM+BK72PT9dy1/+4JqBOcuy4vT7755zfjrIZz8W+ctw41rXHm+lhrAeDulWkUSBB EEk6RbeKzovdOE3nvUCO4QiZsxyvbxaN60le3nQtbrAu65hq2f L6Z+UDLssWDM/xRgBUkhX/HiDHttf/juVVKXu6m6ZWjiv7oRXgrLKfJlGPJTZtc7Px1TJx4lZXLaKRZx s53/q14e9Wr6O3Hx7yRHUKWNvxtVpG81DimN9VOfQPf27H18Djc4CX rOsicfAH5+2NdP6JRX6Nx2BxTBse7HvLseWFzHjMOdJfjpeLUV qNneVYyhN5j32YkGM4QuYrx+UNQ3fBzC1vIC7rmGpZOXw5zhNR 632xrVvTenTXf0N9dUlEnJnvGzo5Lh9M2sTWQtQ7BWO9bce66h KGdUurzUNEvzx1y3FLK55rftvW7yJCQ4/P3uUuH4IyN3/odR6McP7JEDk2ONqO5HisrhW95XisVuM+cmzzxCrSIdFbB6iub 5mSWFfBuoPb+Gqu7QAdow8kwG6YrxxnrS14plaz/uuYalnZgxznkmleZdbC2O9zSjl2azkO4qzyerZR15prt20DTKc EtN7Yq/syshxXWhC7RcOwbGcR+suTc371G3CX497bKv+kX7lrA8VGleO2 su9Ljh0fYLu26SrHHddUW/rJ8YitxpO2HHeuY5O8MimrgRaZZFmy6XNj6P80TI43B5CKE8my TLKk7Oc1/JUAwNjMVo47Lq5WD9su65hq2Uo5VJKtrhlZJlnePjDMatmC7Ry Xr0jbwnTznUCOTYJrI8eyuXe0X7vtpWt939AMClpts6UOai1cY 8uxTdkqrFuaDd1IWtbf1Sru1PfZlN+W9ffpc1xsbPxuFS2t5Ot 6GEOOreppH3KcS1Z2E7M9R7u26SzHw/a7pJccj9lq7CzHptdFTpWkebLIM9m+bxieQEaQY+OB53wgAOwG 5Phw5HhbUDVvuVyWLRg3xyPIsUo2Yl82HqjYvQ/r+uel7FfKtHXt1ixjLKaFgBpkqN5AM74ca7fb2le5MaCvbdYL6 T5vWn/vmt9VAYtlE0nKgV9Ws4EY96C/HFuXW9O1pI8cu9TTkP2rbdpSjjWh76c/wYA8wwFq9zawnR5yvJrabaxW4045rs1WUbm4V57y1wfNOvLKaj SVbX1wGg6uwXJcnjCxZHkueS2KEdIjjLYEGBPk+ADkWLdM2wAs x2V3J8ft1+22G2/rg0CnHGuEdmo51jayVPqgdtaVdsPjthxvFqi8wWzea/XrHyTHDg9v9RkYTALfcVw19rW3HNuWu6U/+6Cp3DoecnvvX23TlnK8drSyi6jhmBkqx7owrGsvcrxchBIEoS yW44hxpxw3Doatk6HyOkj7mk+TEHPF5ZJniSRbU+OMLccWryqR YzgwkOPDlGMRsRtAZrHszuS4x3V79XPDLBEOcrw1mn6EbhVdr4 GbXQG3/246Oe73irryetxQplHk2Da/mvJtWroreeo6rhrrGK9bha7czQegehmHdauwqac9dKvIWx4sd9 atYi9yvJRFGAz+6MfQPsduNC905gPW/IQ2kRyrRNNyXMTAvQYYm9nKsU8D8ky49NVsWfbg+xzXymQj0x3 d8+JswIA8h4FHun7ItfqfakCe+6CoKm31MIkcr7Zq3XI/rN/xmAPyRJrlro5jqs8fvOkSsj1Pb//tjbJ/tU33G5CnfWPRsnznNmchxyMPxNuNHEtdZlvntGx+2cW9W8X2U1 xLt4p9ztUM4Mh85bjtfLO9EbusY6pl24o3lRzbzVZh2NDhynFV HpPte4Jp7mntuq0GHlXqQnuvGFmOXQcuGpiNHPd607obOe6K1h bkGcqxuVw9yzsLOU4jGbtLxU7kuNI6k2i/vW1qvcn0n19skWPTU5RpQJ5+JG+uGRwIsF/mK8fm800rQZVrT95zHVMta9g7y1fR3cse3IA83b7rhMhZjivrC XQCuGkx11+jN7/f9u72FjV9a3u3GFhLbqVLolXZ8sTwqfJ2mZlktorK323ym0uil OZz3fla3g+i5dhW1EeZys1mewc2z3Hl51vHs7Rc7+YwW8XYs1T sTo4rSelIZjmtWlIblGAhx5WbTvm6RJVTtbXNmFFsM2mMwuUDI 3BozFmOa90DVHOAr/78bjtnO9cxybKrh3Vl+lxrc8CX9bIbxvhCXrY1ZqPy6WXjnMjN 1RhGqFcGZbt+BERH9b5gFMnmPnR9ArizRU33u/a3lEZpNn4+2jBYq63LR/X4c/hEcR95cstv9bPLSrOvbl/Isz8+W95Q2B6XOoxyPMb2Bu5fdU1D8mvs7lL/JPtWLnWzj7TNVhHHmoemnc9zPE1/413JcfXb7PqDN99MD1MmLiufTG3kWGTVSb5yUSmexrXrKLbZ/AhIoHTJBtg/85ZjWZ2fjXNcxe1f79r+neU6Jlo2z5JNn8X1NaPZHcx92ZLhOe 7qo+zYbWArNIOyq8u7tjq1tbKKiOTFNVpZlMFim+ZuCj1bjpuh 1EqAXMtWHH/VdapiNiUTo3whzza/5SwaSrOvxhLqcDk+W2TVuty6IvSQY+vtDdy/CoPkuHVQa3NGlI5cts7YYe7bvLcv5E0uxwBwUMxejqETcgxjMk bfz70xdgPdsW9vR1jP8NMBcgwAViBO/kOOYVRGasXbByvJ2p3Y+7693dDytsIR5BgArECc/Iccw7iMJyu7xTDlK9s7bEZ8GEOOAcAKxMl/yDGMjvEbAIfLqlV1d1Ot+r69XWEe3+UOcgwAViBO/kOOAQCQYwCwBHHyH3IMACAS/PnzR15fX+Xl5UWen5/l9+/f8uvXL3l6epKfP3/K4+OjPDw8yP39vfz48UO+f/8ud3d3kmWZfPv2Tb5+/Sq3t7dyc3Mj19fX8uXLF7m6upLPnz/Lp0+f5PLyUi4uLuT8/Fw+fvwoHz58kLOzMzk9PZX379/Lu3fv5OTkRN7e3giCIAiCIAhir0HLMQBY8fZGq6LvkGMAALpVA IAliJP/kGMAAOQYACxBnPyHHAMAIMcAYAni5D/kGABA5P8BR4nZyzVz4HoAAAAASUVORK5CYII=[/img]

Of course this doesn't say anything about whether there's a design flaw, it's just pretty strong evidence that there is something different about the MAX than the NG.