# Results

Welch Two Sample T-Test Students' T-Test
p = 0.005428 p = 0.003642

data: dat\$Tasks.w..5s and dat\$All.Others t = 3.3403, df = 12.791, p-value = 0.005428 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 1.976024 9.246208 sample estimates: mean of x mean of y 10.863968 5.252852

## Hairball Map: What might happen outside of Turk?

### Scenario Development Example

1.Worker submits work GENERIC
2.1 Requester mass rejection parameter kicks in GENERIC
3.1 Requester team screens rejected tasks [Account 46]
4.1 Requester team submits results report to Worker [Account 46]
5.1 Requester team posts to worker review page [Account 46]
2.2 Requester sends verification email (UNKNOWN) [Account 56]
2.3 Requester sends automated email [Account 62]
2.3.1 includes a task ticket confirmation [Account 17]
----[INCOMPLETE INFORMATION]---
---Begin Generic Email Response---
1. Worker writes email to requester GENERIC
2.1 Requester responds to email quickly GENERIC
---something happens---
2.2 Requester does not receive email GENERIC
2.3 Requester marks worker's email as "spam" [Account 17]
NOTE: 17 is vengeful worker. "make sure I was paid my 20 cents".

Might have acted in a way to have pushed requester to mark emails as "spam".

## How might Requesters manipulate tasks as a response?

These strategies are areas of control for the requester to achieve an unknown goal with similar tasks posted sequentially. Workers monitor requesters for these changes.

---CONTROL---- ---ACCOUNT(CASE)---
1. Increase/Decrease Pay 17
2. Introduce Test Screeners before task 30
2.1 Announced/Unannounced
2.2 Paid/Unpaid
3. Task Qualification Constraints In/Decrease GENERIC
4. New Task Attempt Recreation 27
5. Control/Block Emails 17
5.1 Mark all email communications as spam
5.2 Mark partial emails as spam
5.3 Mark none
6. Avoid posting more tasks GENERIC