A Type I is a *false positive* where a __true__ null hypothesis that there is nothing going on is rejected. A Type II error is a *false negative*, where a __false__ null hypothesis is __not rejected__ – something is going on – but we decide to ignore it.

In this case, the software designers were trying to optimize the ability of the car’s autonomous systems to recognize humans and other obstacles so that the car did not slow or stop too often due to things like lane pylons, trash in the gutter, or street signs on the side of the road. If that happened, the car would have a jerky, uncomfortable ride and slower average speed. Recognizing a lane pylon or sign pole as an obstacle to the car would be a false positive, a Type I error. (O’Kane, 2018)

Understand this is fuzzy, complex science and the system designers had to integrate the information from multiple sensors. One sensor might “see” a data point as an object better in the dark than another. Still another system might “see” an object and project the object’s path to be stationary while still another system might calculate a motion into the car’s path. Most of the systems use artificial intelligence that had to be trained using thousands of “images” that may or may not be similar enough to this victim walking a bicycle. Logically, some overarching system has to evaluate all the data and make a decision that there is an object that is an obstacle in a threatening path or position and initiate evasive or braking actions.

Again, if the “master” system was too conservative in its decisions and registered “positive” for objects that were not threats, it would produce an unnecessarily jerky ride. My assumption is that the system designers set the detection program’s parameters to make false positives less likely, to ignore an object until the system was very sure it was a real obstacle to the car. At the same time, the system needed to not have false negatives, failing to recognize objects that were a threat. Finding the optimal setpoint is akin to picking a significance level.

Trying to minimize false positive is analogous to decreasing the alpha, the level of significance, so it is more difficult to reject the null that “nothing is going on here.” Because Type I and Type II errors are “connected,” as you make it more difficult to have a false positive, Type I error, you also make it more likely you will have a false negative, a Type II error.

After much review, Uber now says the autonomous system did “see” the pedestrian and first classified her as an unknown object at 6 seconds to impact. It then thought she and her bike was a car, and finally recognized her as a person about 1.3 seconds before impact. By then it was too late for the normal control system to react and Uber had disconnected the Volvo emergency braking system. For testing, the company had backup human observers who were supposed to recognize mistakes and take appropriate action. That backup failed at a crucial time and the autonomous system was on its own. (NTSB, 2018)

One way of thinking about this is that the sensors in the car actually detected the woman who was struck but “decided” initially the data was not sufficiently strong to register as a real obstacle and stop the car. The p-value it calculated, if you will, was greater than the alpha the system designers chose, and the system did not reject the null that “there is nothing of concern happening here.”

But the system continued to collect data, increase the sample size n so to speak, until the test statistic crossed into the rejection area, though that was too late to save the pedestrian.

The system initially made a Type II, false negative, decision and failed to reject the false null that the “object is not real.” (Marshall, 2018)

**The Key Takeaway**

My point is that when you decide on your level of significance in the real world, you must consider the costs of a mistake either way. You must evaluate the consequences of making a Type I, false positive decision or making a Type II, false negative decision and set your significance level appropriately.

Efrati, A. (2018, May 7). *Uber Finds Deadly Accident Likely Caused By Software Set to Ignore Objects On Road.* Retrieved from The Information: http://bit.ly/2KquXbH

Lee, T. (2018, May 7). *Report: Software bug led to death in Uber’s self-driving crash.* Retrieved from ARS Technica: https://arstechnica.com/tech-policy/2018/05/report-software-bug-led-to-death-in-ubers-self-driving-crash/

Marshall, A. (2018, May 29). *FALSE POSITIVES: SELF-DRIVING CARS AND THE AGONY OF KNOWING WHAT MATTERS*. Retrieved from Wired: https://www.wired.com/story/self-driving-cars-uber-crash-false-positive-negative/?mbid=social_twitter

NTSB. (2018). *Preliminary Report Highway HWY18MH010.* Washington D.C.: National Transportation Safety Board.

O’Kane, S. (2018, May 7). *Uber reportedly thinks its self-driving car killed someone because it ‘decided’ not to swerve*. Retrieved from The Verge: https://www.theverge.com/2018/5/7/17327682/uber-self-driving-car-decision-kill-swerve

Recall that the equation for the standard error is

where σ is the population standard deviation and n is the sample size.

One can find any number of precise “academic explanations” of why this is true, and I give my students links to those references. But I often get follow-up questions from students who look at those references and then ask for a simpler, clearer explanation that does not involve a lot of algebra.

So, I am going to attempt one here and in the companion video.

Let’s also assume that each roll of the dice is a sample of size n=1 since we just have one die. We know from basic probability that we need to look at the long-term when we are looking at empirical probabilities, so let us make 10,000 rolls of our single die. So, we then have **10,000 samples of sample size n = 1**.

I am going to simulate doing 10,000 rolls of a single die using basic Excel. Here in Figure 1 is a screenshot of my worksheet with most of the rows hidden.

*random*integer between the limits shown (inclusive of 1 and 6) every time the worksheet is recalculated. I copied this formula down the range B3:B10002, as you can see in column C where I show the formulas in column B. When the worksheet is recalculated, each of those 10,000 cells calculates a new random value between 1 and 6, inclusive. Thus, we are simulating 10,000 rolls of a single die. Each cell in the range B3 to B10002 contains the face or value on that die. Each is a sample of size n = 1.

Although calculating the mean, x-bar, of a sample of 1 is a bit trivial, I do that in column D using the Excel AVERAGE function. I copy that formula down the range D3:D10002, as you can see in column E where I show the formulas in column D. For the first sample, in cell D3 the average of the value on the single die, 3, is just 3.000. I am doing this to establish the format I will use for all the remaining sample sizes from 2 dice to 30 dice.

In cell F3 using the Excel AVERAGE function over the cell range D3:D10002, I calculated the average of the sample means of the 10,000 dice rolls and that is 3.483, which is very close to the actual population mean of 3.500. [(1+2+3+4+5+6)/6] I also calculated the standard deviation of the means of those 10,000 samples in cell H3 using the Excel function STDEV.P over the cell range D3:D10002. That value is 1.70971, again very close the actual population standard deviation, σ, of 1.7078. [The standard deviation of 1,2,3,4,5,6 = 1.7078.]

I could have simulated many more throws and would likely have gotten my “averages” closer to the theoretical values, but these 10,000 samples should be enough to give you the idea of how this works.

*relative frequencies*of the value of the 10,000 samples of size n = 1. They are not too different from the theoretical 16.666667%. I used just six

*bins*because there are only six possible values for a throw of one die. I also show a line to represent the population mean of 3.5, though you cannot get a mean of 3.5 rolling just one die. The uniform spread of the values explains why the standard deviation sigma is relatively large compared to the mean.

Next, I will repeat the simulation using 2 dice, which is a sample of n = 2. Figure 3 is an image of the Excel worksheet constructed for a sample of 2 dice as was done for the first example of one die, a sample of n = 1. You can consider this to be the sampling distribution for two dice rolled 10,000 times. In column D, I have calculated the mean, x-bar, of the two dice, which is 2.500 (cell D3) for the first sample of n = 2. In column E, I show the formulas in the adjacent cells in column D. In Cell F3, I again calculated the average of the sample means, x-bar, for those 10,000 samples of n = 2 and that value is 3.496, again close to the theoretical 3.500 population mean. This is what we expect if we believe the Central Limit Theorem is true.

**This is our standard error, the standard deviation of our sampling distribution for the mean of two dice.**And you should notice it has decreased quite a bit from the standard deviation 1.7077 we had for a sample of size n = 1.

Be sure to note the difference between the number of samples and the sample size, n. **Here the number of samples is 10,000, but the sample size is just n = 2.**

This next graph (Figure 4) is the relative frequency histogram of the average value of the two dice for the 10,000 samples. Note the distribution is beginning to resemble a “bell shape,” which is what should be expected if the Central Limit Theorem holds true. Also note, I increased the number of *bins* in the histogram to better display the distribution.

I repeated the Excel simulation for rolling 3 dice 10,000 times, and again for 4, 5, 6, … all the way to 30 dice being thrown 10,000 times, but I will not show all that information here in detail. I will use it for the very last part of this discussion.

**Remember these are 10,000 samples of size n = 5.**In column G on this worksheet, I included a calculation of the average of the 5 dice using the Excel AVERAGE function again for each of the 10,000 rolls. The average of those 10,000 sample means is 3.489 in cell H5, again very close to the actual population mean of 3.500.

And the **standard error of these 10,000 samples of n = 5 is 0.764**, calculated in cell I3. Recall that is the standard deviation of the sample means in column G, which is our sampling distribution of sample means.

Repeating the simulation using 10 dice, 20 and 30 dice, we get these three relative frequency distributions for the 10,000 sample means for samples sizes n = 10 (Figure 7), n = 20 (Figure 8), and n = 30 (Figure 9):

In the table in Figure 10, I have captured the relevant data from all the simulations I ran using this method in Excel. I also show a scatter plot of the data with the calculated standard error on the y-axis and the number of dice – the sample size n – on the x-axis. The orange dots are the individual data points for the 30 simulated standard errors.

Obviously, there is not a straight-line relationship here, if you recall plotting best-fit lines from geometry or algebra. Fortunately, Excel has a great tool that will let you select from lines that could fit your data. These are known as trend lines, or regression lines, and you will learn how to calculate them later in your intro stats course. For now, trust that Excel can find a “best fit” line for these data and calculate the equation that describes it.

Figure 11 shows a “Straight-line” or “Linear” best-fit trend line (small dots), which does not fit our data (large dots) all that well, as you would expect. The equation of this line Excel calculated is shown as y = – 0.0278*x + 0.9766. You can use that equation to predict values of y, the standard error, for different values on n. Of more interest now is the R

^{2}value of 0.6345. R

^{2}is the Coefficient of Determination, again a term you will learn a precise definition for later in your course. For now, I will say this value tells us how closely the equation predicts the actual values. The closer R

^{2}is to 1.00, the better the equation predicts the actual values. Here, this straight-line equation only accounts for about 63% of the variation in y, the standard error, which is not very good.

Of the options available in this Excel tool, the best “best fit” line to my eyes is called a “Power” function and this next image is the graph showing that trend line which is shown in Figure 13. The Power function Excel found to best fit our data does look pretty good as our 30 data points fall “precisely” on the calculated trend line. Excel gives this power line equation an R

^{2}of 1.000, though it is not really exactly 1.000 as there are likely digits we do not see. But it is close enough that Excel rounds to 1.000, which means the equation does an excellent job of predicting the standard error given the sample size n. If I continued to simulate the standard errors for sample sizes greater than 30, they too would plot almost exactly on this power line.

A Power function is an equation where the value of y is a function of x raised to a power. In this case, y is equal to 1.710 multiplied by x to the -0.5 power.

Recall from algebra, a negative coefficient just means we can put x in the denominator and get a positive coefficient,

Raising something to the 0.5 power is the same as taking the square root. Then, this best fit line’s equation is

This 1.710 is very, very close to the actual value of 1.7078 for the population standard deviation, σ, found earlier. If I had run even more than 10,000 samples, it is likely the numerator would have converged on sigma. Since x is our sample size, n, we have essentially shown that the sample standard error is

Again, in this exercise, I used simulations, a lot of them granted, to approximate the standard errors used by Excel to calculate the power function equation. Use this practical explanation to supplement the more precise algebraic proofs. I hope this gives you a better feeling when your instructor of textbook tells you the “proof” of why we divide sigma by the square root of the sample size n is beyond the scope of your course.