ShapCap’s Summary Initial Thoughts- - Opening line: tells readers that the OP started is considering this with the express intent of pointing out a failure in software development. Sets the tone. - Does not have all the available data - Uses “feelings” to determine validity of a refund. No objective qualifiers or rules. Does not provide method used to validate refunds. - Does not discuss what method of statistics was used to arrive at conclusions. - Average appears high, based solely on what is shared daily on SR. It is uncommon for 2K+ refunds to be seen, based on personal experience. Most are for several 100, if not lower. Discoveries – - Draws improper conclusion that because more people are getting refunds, more whales are also getting refunds. I disagree with this because the mentality of a whale who is backed at a high level is not necessarily the same as a backer who has spent less money, and they occur less frequently. - References a “whale” but never defines what qualifies a backer as a whale. - Uses unverifiable average that is used HEAVILY to draw conclusion. - Has a LARGE math error in spreadsheet, cell B18. Total from linked page is 33,581, not 97,824 as listed. That is a difference of 64,243. That has an overall effect of throwing the total off by 9.11%, which is WAY MORE than statistically significant. Since this is an early datum, the effects of this error will be felt throughout the entire report. - Uses an average refund of 2,088.19 to fill in the blanks on over 80 “unknown” refunds. It is unknown how this average can be accurate if data is missing from 80 out of 336 refunds. That’s 24%, or almost a quarter of data used. - OP assumes that the rate of refund growth will continue to grow at the same rate, despite the data showing that there have been several months where both the $ refunded and amount of refunds issued have dropped month to month. They do eventually go up, but the prediction that there will never be a “refund slump” is bold. - Multiple Entries for same user – Approximate error amount: $ 24,407.76 (3.46% of total amount) o CrushBoss A-11,000 on 8/14/16, B-10,818 on 9/30/16 o Drpogue A-unknown (2,088.19) on 3/31/17, B 1,330 on 4/4/17 o GaryOaksHotSister – A-unknown (2,088.19) on 9/20/17, B-unknown (2,088.19) on 9/20/17 o LocalBanuTrader - A-unknown (2,088.19) on 9/10/17, B-unknown (2,088.19) on 9/14/17 o Majortell - A-unknown (2,088.19) on 1/20/17, B-unknown (2,088.19) on 1/27/17 o Rentusi – A 322.00, 5/23/17, B-503.00 5/11/17 o Spbubsy – A 1,200 on 2/3/17, B – 1,060 on 2/9/17 o TCGameFan – A-unknown (2,088.19) on 9/7/17, B-590.00 9/19/17 o Warhawk109 – A-3,350.00 3/11/17, B-3,235.00 on 3/17/17 o Woosh-Sky – A-365 on 3/15/17, B 365 on 3/22/17 o
- Data Transcription error – Error amount - $19,000 (2.70% of total amount) o Meowez1 – 6/19/17 Refund listed as 10,000 when actually reported at 1,000 o P149U3 – Has discussed getting a refund, but has not actually received or requested one. Listed as 10,000. OP’s Summaries – - Cannot be given validity due to statistically significant numerical errors that skew the data. - Cannot draw an accurate conclusion based on the data presented and the methods used to gather said data that were presented. My Summary – - Upon review of data and sources provided by OP I have noticed several areas of concern: o Mathematics errors in the sheet. o An average that is formulated based on mathematical errors. o 24% of data input into the sheet is “unknown” and has had the above average plugged in. o Upon further review, several entries have been duplicated. This results in additional mathematical errors, to the tune of $24,407.76. It is worth noting that in some cases it was not as simple as the amount put in twice. For example, one number was entered as ~$600 for one user and then unknown as their duplicate, which was plugged in as the “average” of $2,088.19. I believe that this is due to an issue with how data was scraped and the data was not properly reviewed after being scraped. This calls into question the validity of most, if not all of the data. o Some values have been mined improperly, to the tune of $19,000. o The mathematical errors have a total value of $107,650.76. That is 15.3% of the reported total. STATISTICALLY SIGNIFIGANT. An error of this magnitude would be an automatic fail on any thesis or statistical report. Why this is important: Earlier today, I cited an article that talks about unknown bias by the researcher having an effect on the results of a study. This is called “Observer Expectancy Effect”. I believe that the OP’s conclusion has been effected by this. They got the result they were looking for and did not do enough data checking to ensure that what they were using in their analysis was accurate and not duplicated. It took me approximately 25 minutes to review the data and write up my conclusions. I would have expected the OP to take much longer than that to verify their data, especially if they are going to declare the impending doom of a corporation.111 The counter argument of course is that I was looking for his study to be incorrect, so I found data that supported my hypothesis and published it. This could apply, except I am using all data, sources, and methods provided by the OP. I just combed through it a bit more, and made no assortations that are not backed up by evidence provided by the OP.

Hope you enjoyed the read! Tagged users from other replies: u/Ranting_Demon u/Yo2Momma u/goon_publicaffairs u/pat_trick