0:00 Who are you? And where are you going? What do you [Music] want? Hello and welcome to People Making Money. I'm your host, Kevin Bemel, and we're here every Wednesday at 10:00 a.m. Pacific time to talk about job hunting, running a business, starting a business, building wealth, all of the financial
0:35 aspects of of life. And uh so so today uh I'm touching on a subject that I mean it certainly applies to our finances since um statistics are so often used to get us to to get us to act in a particular way that we might not act if we understood the statistics better. Um but but indeed this is uh something that I I think affects our lives um beyond our financial lives. Uh and it's it's it's one of my I don't know personal
1:13 bugaboos I guess. And that is uh and and I I I read this book quite a quite a long time ago. It was originally written in 1954. That's how long this pro problem has been recognized. and it's called How to Lie with Statistics by Daryl Huff. So, the fact of the matter is that uh you you may have heard the uh expression there are lies, darn lies, and statistics. And it it seems like uh increasingly
1:50 uh statistics are used to uh hide the truth as often as they are to expose it. And and that's unfortunate. Uh we we we live in a very datadriven society or a seemingly datadriven society. I'm not sure how data driven our society actually is. Um but uh um we have numbers and statistics thrown at us all the time and and yet uh and and it's taken it's taken for granted I think that we're going to
2:26 accept them and yet uh often times those numbers and statistics do more to obscure the the situation, obscure the truth than they do to reveal it and so uh Daryl Huff who you know again wrote this book now it's been uh 71 years ago right so his point was not to advocate for us to ignore statistics really it was for it it the book advocates for us to think
3:03 critically when we're faced with statistics to see if what's being what we're being hold actually has any validity at all. And he identifies a number of, you know, I guess issues, if you will, um that we should be aware of of which we should be aware, uh so that we're not fooled by statistics and fooled by numbers. And so I'm going to talk about those today and and give you some uh examples
3:41 of of just sort of common ways that statistics are manipulated or presented uh in a way that actually misleads rather than informs. so that you can get a sense of what I hope will be uh um like like it's something in the back of your mind will say to you when you hear a statistic, well wait before I believe this statistic, uh what what do I really need to to know or or how do I need to examine it so
4:17 that I'm not fooled into believing something that that isn't true? So, this is designed, I think, to to raise some skepticism. Now, I'm I'm I'm a bit of a skeptic. I think uh I don't know if it's by nature. I don't know if such a thing exists or maybe it's just how I was raised. Um to be a little bit contrary, if you will. Uh but I but I think there there is some benefit uh to not accepting the things we're told these days. uh a at face value. Uh unless
4:55 we're very very sure of the source and um we're also sure that the source has um you know done his own uh work in terms of making sure that uh you know that that he's you know thought these issues through. So, uh, there's there's I believe I I think I put together eight main points. Yeah, eight main points from the book. So, I'm I'm going to go through them one by one so you can see the kind the the ways that that statistics are used to lie. There's
5:32 there's just no plainer way of saying it than that. So, the first one is uh what's called sampling bias. Okay? and and the reality is if a sample is not representative of the whole population the results are meaningless. Now, when uh a reputable polling group, for example, Gallup uh does a survey or say the the Pew Center, one of the things they spend a great deal of time on is developing a
6:10 sample of a size and composition that is reflective of whatever more generalized group they they want to gain insight into. Okay. So, and and and and so and it takes a lot of time and energy to do this. So, I remember when I was in the Navy, uh we had a conference of of all the chaplain in I think sort of the Southern California area. There were
6:45 maybe 20 of us there. And this one particular chaplain had recently done a what he said was a a survey that showed certain things about uh sailors in the Navy more broadly. So the first question I asked him was,"Well, how did you develop your sample size or how did you develop your sample?" I mean, and he said, 'Well, I just took a survey of the sailors in my command. I said, "Well, what is your
7:20 basis for concluding that the sailors in your command are representative of the Navy as a whole?" And he says, "Well, you know, my wife does this all the time." I said, "Well, okay, fine. I said, "So then what is your wife's basis for concluding that the sailors in your unit are representative of the Navy as a whole?" And then he just got mad at me. And and and and here's the problem. He was trying to make generalized statements based on just the sailors
7:56 within his command that uh obliged him by filling out his his survey. So there there's a couple of problems with that. One is and I don't remember what community he was in but let's say he was in the construction battalion community. So those sailors are very different than sailors in the surface navy and sailors in aviation and sailors in special warfare. So just because it's true of the sailors in the CBS doesn't mean it would necessarily be true of sailors in
8:31 in some other uh community in the Navy. That's number one. Number two, uh sailor, younger sailors who may be single, okay, are are likely to have very different a very different outlook than sailors who are married or sailors who are married and have children or sailors who are single parents or sailors who are just older closer to retirement. sailors. Sailors who are enlisted people versus whether you call officers sailors or just officers than than officers. Sailors
9:07 who like and respect chaplain versus those who don't. I it's you know dirty little secret I guess in the you know in the military there are quite a number of people in the military who think that there should not be chaplain in the military. They view that as a violation of the so-called separation of church and state. Right? So there was no there was there was no time spent validating his sample and so therefore his his conclusions were were were were useless. Okay, his findings were useless. So uh he had what was what
9:45 could be considered a non-random sample or there could have been self- selection bias. So the people who responded were the ones who liked him and trusted him. And so that's built into the survey. And knowing that they liked and trusted him, they may have answered the questions in the survey with an eye to, you know, what what would this chaplain want me to say? Okay. So the fact of the matter is anytime we see a survey, the first question we should ask is how was the sample
10:22 constructed? Right? So uh here here's here here's an example. I I used chat GPT to come up with some examples um that were just kind of generic but but easy to understand. So, a luxury watch brand surveys only its newsletter subscribers and reports 92% of our customers would recommend our watches. Can you see the problem with this? So, it's the people who have already said we like your watches that were surveyed. So, of course, they're going to get 92% of them recommending
10:57 them. Right? In I mean, it would be it wouldn't be a surprise to hear 100% of them did, right? I mean, but it's obviously going to be these are people who have already expressed a liking for this particular product. It shouldn't be any surprise that the number is so high, but that says nothing about how people in general like watches, right? So, so there in lies that's like saying, you know, it's it would be like, you know, for Apple Watch, if they surveyed only Apple Watch owners, you know, 92% of Apple Watch owners like our product or
11:34 would recommend our product. Well, sure we would because it's self- validating if other people buy something that we already have. That says nothing about uh you know the whole universe of uh you know Fitbit owners and other smart watch owners who many of whom detest Apple Watch for various reasons. I know I have a friend who does he owns one but he still detests he distests all things Apple but anyway um you can see there's an inherent bias in a sample and so for
12:10 therefore its claim is is meaningless. So whenever we hear of a survey we should always ask uh who was surveyed and how was the sample selected. If we don't know those two things, we probably should discount whatever that survey is claiming. Okay. Okay. So that that's that's the the the first uh point. The next point is misleading averages. So there are generally speaking three types of averages.
12:47 There's what's called the mean, which is typically what we think of when we hear the word average. And I'll I'll explain how each of these are calculated in a moment. There's the mean, the median, and the mode. Okay? So, the mean, if you remember this from, I think, elementary school math class or maybe junior high, with a mean, we take all the um items in the group, add them up, and then deni and then divide by the number of items. So let's say let's just take a
13:22 real simple example. Let's say we have a a group of figures 1 2 3 4 and five. If we add them together that's 1 and 2 is 3 + 3 is 6 + 4 is 10 + 5 is 15. We divide by five. 15 / 5 is three. The mean is three. Okay. That's the what we call typically call the average. Okay. We added all the figures together divided by the number of figures. So 1 2 3 4 5 added together 15 divided by three I'm sorry divided by five gives us three.
13:58 Now the median is the if we put the figures in order 1 2 3 4 5. It's the one that's equidistant from the highest and the lowest figure. Now, interestingly enough, in this sample, 1 2 3 5 4 3 is in the middle. So, the median and the mean are the same. Okay, so the median is the one that's in the middle of the sample when they're ordered highest to lowest or lowest to highest. Okay, the
14:34 mode is the item that appears most frequently. So in this sample of one, two, three, four, five, there really is no mode because they all appear once. But if we had a sample that was 1 2 335, the mode would be three. Uh, by the way, so would the median. Now the mean would be 14 / 5 which would be like instead of three it would be like
15:10 2.9 or something like that or 2.8. But you can see in this case because of the because the sample is kind of evenly distributed the mean the median and the mode are all pretty much the same. Okay. Now here's here's where it gets dicey. Often times when when someone is using statistics to try and make a point or sell us something, okay, they will choose which of these to use that best
15:45 makes their case as opposed to the one that is most descriptive of the actual situation. Okay. So whether someone chooses the mean, median or mode can change pretty dramatically the results. So for example, the CEO of a company says our employees on average earn a salary of $120,000. That sounds pretty good, doesn't it? Now, that's probably because or that may be because if you take the
16:23 mean, you get $120,000. And and and that's because you can you'll have the senior executive earning huge salaries, maybe $2 million plus a year, but then most of the staff earn salaries of maybe 60,000 to 80,000. So what happens is is this group with the super huge salaries at the top, they pull the mean up from, you know, to $120,000. Even though if you were to look at the
16:58 median salary because so many people earn between 60 and 80,000, the median is really more like $70,000 because maybe you have 10 people earning 2 million plus, but then you have 30 or 40 people earning 60 to 80,000. So, while it it may be numerically accurate to say that our employees earn on average $120,000, it's really misleading because on average the median average is $70,000.
17:36 And that's much more descriptive of what people at the company actually make because most make between 60 and 80. So do do you see how in this case a misleading average is being used to hide the situation not to you know bring light to it so to speak. Okay. So uh means the mean can be skewed by outliers. The median oftentimes tells a much clearer story. So that's why for example when we talk generally speaking when uh the the
18:14 Federal Reserve Bank and and other organizations do studies of home prices they talk about the median home price because otherwise the the you know the multi- tens of millions and even hundreds of millions of dollar valued homes skews what is what is a truer picture of the home price market in the country. Okay, even in a state like California where we do have a lot of high-priced
18:49 homes or New York, uh still median is tends to be much more reflective of the actual market as average people like you and I experience it. Okay. Uh whereas if we were to take the mean or what's usually what's what's usually meant when people say the average uh that would skew it higher. Hey, we do the same thing with median income. So, you know, be wary when you hear about average income, okay? Because the fact of the
19:25 matter is there are people in this country who make enormous incomes and not necessarily every year but they may they may have a an enormous income in a particular year. Let's say they they sell their business and so now they you know they they sold their business for you know $10 million or or $100 million and all of a sudden so that huge income increase for them that year. those kinds of outliers tend to skew income up. Okay? And so therefore, mean income or average income is not as descriptive as
20:02 median income. By the way, we see this on the lower side as well. Right? So for people in the bottom quintile, the lowest 20% of income earners. Okay? Um most income statistics do not include government benefits they receive. Okay. So this so they're the income that's reported for them which may be you know zero. Okay. That if you if we went by
20:38 the mean would tend to pull things down because again those are outliers. The reality is if if if their income were reported more accurately and people there are people who make up to 60 $65,000 a year through various government benefit programs. If that were included in the statistics, mean or median, we would see that the lowest quintile is actually doing about as well as uh the upper second quintile. Okay,
21:16 so it's so again, we need to be very careful about when we're hearing averages, how were those averages constructed? Is it a mean? Is it a median? Is it a mode? Okay. uh and and and then and and and be skeptical, you know, when we're looking at those average figures. So again, with with misleading averages, anytime we hear an average, the question should be which average is being used and why? Okay. All right. Next, small sample sizes. So small
21:54 sample sizes can lead to exaggerated uh results um because there really isn't any randomness there or there may be so much randomness there that it's it's completely meaningless. Okay, this is this is kind of related to sampling bias but but now what's happening is the sample is so small that whatever result is being reported is as likely to be a statistical fluke as it is to be valid. Okay. So, uh, that's that's why when when reputable
22:32 um surveying firms I like I said I mentioned Gallup, they spend a tremendous amount of time on both the size and the composition of the sample size for a survey because it's in those two factors that the validity of the survey will rest. Now, it may seem amazing that in a country of I think the United States has got what 360 million 370 million people in it, we can have a survey of I think you know 15,000
23:08 people that would be representative of the country. Okay? But just keep in mind that that survey is if it's done by an organization like Gallup is very carefully constructed. Okay. And and so while while we're talking here about small sample sizes, small, you know, it it's somewhat relative. So 15,000, which would be, you know, less than a tenth of a percent of of our population. Nonetheless, that's still a pretty large sample size and,
23:46 you know, could very well be statistically valid for a nationwide survey. Okay. Um, on the other hand, when we're dealing with truly small numbers, okay, let's say, so for let's say a a startup, there's a startup and it says, you know, our app increases productivity by 40% in a recent trial. Okay. But if you dig deeper, their trial had, you know, five users, all of them were members of of the team that developed the app. So the the the size the sample size is too small and biased
24:23 to draw any kind of generalized conclusions. Okay. So, so again um we you know when we when anytime we hear about some kind of statistic like you know a recent survey showed you know blah blah blah blah blah the first thought that should come in our head is what was the sample how big was it how was it constructed okay all right next percentages and probabilities so um uh uh Daryl Huff shows that percentages can be
25:00 manipulated to sound more dramatic. Okay. So, so for example, you'll you'll hear sometime like this, you know, there's a 50% increase in risk uh from doing the following. Okay? But that could mean going from two people out of a thousand to three people out of a thousand, right? So yes, going from two to three people is a 50% increase uh in terms of the people affected, but three people out of a thousand is still.3% people affected versus 2%. In
25:37 other words, it's not alarming at all that there was a 50% increase. Okay? So, so one needs again whenever we hear a percentage, one needs to be okay. Okay. But how was the percentage calculated? Okay, so you'll hear like you'll um like a skin care company might say, you know, our product reduces wrinkles by 50%. First of all, what does that even mean? Does it mean 50% less wrinkles, so there was 20 here and now there's only 10 or
26:12 does it mean, you know, the the wrinkles were two millimeters deep or 0.02 02 millimeters deep and now they're 0.01 millimeters deep and so now they're reduced by 50%. I mean these things are oftentimes just completely meaningless. So anytime we hear a statistic expressed as a percentage, the first thing we need to do is say, okay, but what are the numbers behind the percentages, right? And why are they using percentages instead of telling me the actual numbers? Okay. So, uh you know there
26:52 used to be a famous commercial when I was a kid for Crest. I think it was Crest toothpaste. Four out of five dentists surveyed preferred Crest, right? I always thought that was really impressive when I was a kid because we used Crest toothpaste. I'm thinking, "Wow, you know, four out of five dentists. Okay, well, we're really doing the right thing." I mean, what does that mean? How many dentists did they talk to? Did they talk to 10? And eight out of 10 dentists preferred Crest, right? In which case, the thousands of other dentists weren't even consulted. So, we've got too small a sample size for it
27:28 to be um you know, relevant uh or you know, you know, and what does it mean that they preferred Crest? Right? So, do they prefer Crest over, I don't know, baking soda? You know what I'm saying? This is how right um did they prefer it over Colgate and Pepsidin the actual competitors to Crest or did they prefer it over not brushing at all or brushing with no toothpaste? Right? So there's all kinds of ways that percentages and probabilities can be manipulated and we
28:03 need to be skeptical when we hear them. Okay. Next, graphical distortions. So often times charts and graphs are used to manipulate us visually rather than inform us. So one of the ways this is done most often is and if you can picture a graph where you have the the y axis and the x axis if you remember how those work right and you have a you have some line like this or whatever right so depending on the scale of the axes that
28:39 can very well determine what the results actually look like. Okay. So, so for example, if we want to show um you know higher growth for example, so we can we can make the x axis start at 95 instead of zero, right? And and that can make it look like there was a huge, you know, growth surge when in fact it was actually very very little. Okay. So, um whenever you see a graph, the first thing to do is to look at okay, what are
29:17 the baselines of the graph? Okay. So, check the scale of the presentation. If it's if it's years, why why did they use you know those particular years? Okay. Why did they choose um whatever um you know digits, thousands, millions, whatever they chose to make, you know, to be their scale. Why did they choose those particular numbers relative to the data presented? Okay. So, so again,
29:53 always be skeptical when you see data presented in a chart, especially if wow, that re that really makes the case right there. If it's if it so clearly makes the case, that's the time when the alarm bells should be going off and saying, "Wait a minute, wait a minute. How how how might I be manipulated?" Now, I will say not not all graphs are manipulative. Okay, the idea here is to just help us take a pause, really understand uh what we're at what we're looking and then draw our conclusions rather than just
30:29 looking at it and immediately drawing our conclusions. Okay. Next, causation versus correlations. This is one of the I think this is my biggest uh bugaboo about statistics is the implication that something that is correlative is causitive. Okay. So, uh the the example I have here is okay um ice cream sales in the summertime go up.
31:04 It's hot okay in most places. And so ice cream is a a wonderful food to eat when it's hot. Okay. Also, drowning tends to go up in the summertime because it's hot and swimming and being in the water is a pleasant way to get relief from the heat. Okay. So ice cream consumption or ice cream sales and drowning both increase in the summer, but ice cream doesn't cause drowning, nor does
31:39 drowning cause ice cream sales to go up. Okay? They correlate, but they have no causation. They have no causitive um relationship at all. And we see the I see this abused it seemingly all the time. So people will say to me, well gosh, I'm trying to think of another example here on the fly. Uh they'll say to me, um ah god, it's not coming to me. Okay. Uh um
32:14 so um oh, so here's here's another one. Here's another example I've got. Okay. So um and and the point here by the way is that two things can be related without one causing the other. Okay. So um a marketing report shows that customers who visit a company's website also spend more in store. Okay. So so then they conclude therefore our website causes higher sales. Well maybe. But maybe it's just that people who go on
32:50 the website are already predisposed to buy more. Okay, the website has nothing to do with it. It's just they're they're more engaged overall and so they go on the website, but the website may have nothing to do with it. It may be a correlative relationship, but not a causitive relationship. Okay. So, um uh uh I I had another example in my head
33:26 and now it's gone away. Oh well. Okay. I hopefully it'll come back to me while I'm while I'm talking about the next one. Okay. But at any time that you hear that something causes something else, okay, stop right then. far fewer things are causitive than are just correlative. Okay? And and that's where we need to make the separation. So anytime you hear, you know, X causes Y, be wary. Okay? On what basis was that conclusion drawn? Merely because it's like ice
34:03 cream consumption and drowning, they both go up in summer. Okay. Completely invalid in terms of causation. Okay. Or is it that uh you know um someone who who has a headache and and takes Advil, their headache tends to go away. Okay. So that's it's probably the Advil. You know, there's a good chance that the Advil caused the headache to go away or reduce. Why? Because ibuprofen, which is the active ingredient in Advil,
34:40 um, you know, causes the physiological physiological response that causes pain to go away. Okay. Now, it does some it does some other things like it can it also tends to um cause liver problems. Okay. Just like acetaphinamin um Tylenol acetamin is the active ingredient in Tylenol that tends to cause stomach problems especially if you don't take it with food right so those are causitive now there was you know hundreds of millions
35:15 if not billions of dollars spent on developing these drugs and testing them and seeing whether they're efficacious uh in in their causitive claims. Okay. um as well as in their negative effects, right? So, so we can be relatively sure um that that in those cases causation is in fact present. Okay? Uh but um you know uh so I I remember I I it seemed like
35:50 every time I took uh I think it was ibuprofen I gained weight. I happened to mention that to my uh my doctor one time and he said yeah ibuprofen is not causing weight gain. there's no there's no basis there that may correlate and it may be that when at the times when you take ibuprofen, okay, you're doing something else that's causing you to gain weight, but it's not the ibuprofen, right? And he says, so don't refrain from taking
36:25 ibuprofen when you need to because you're concerned about weight gain. Those two are not causitively related. They're only correlated. Okay? So and and we we get these things in our heads that one something causes something else when in fact that is that is not true in the least. Okay. Next uh the what what Huff calls the semi-attached figure. Okay. So the trick involves using a a statistics that's technically true but it's it's irrelevant. So um so a product will say
37:03 you know such and such or an advertisement I mean will say such and such a product is is used by doctors okay and it doesn't say how many or in what context so who cares I mean you know doctors are you know in my experience generally speaking nice people and all of that but but who cares right so you see this for example in in vitamin advertisements so a company will that sells vitamin C will say something like vitamin C is essential for immune health. Okay. And our supplement
37:40 contains 100 milligrams of vitamin C. Okay. Well, so they're right about vitamin C. It appears that vitamin C does improve uh immune health. But just because their product I includes vitamin C, that doesn't mean that their product is is effective because it may not have enough vitamin C to support immune health. Number one. And number two, it may not be any more effective than any other product that contains vitamin C.
38:16 So the the statement is essentially irrelevant. Okay, who car? Okay, you know, yes, yes, part A of the statement is true, but that doesn't mean part B of the statement is true. That's why they call the semi-attached figure. So part of it is true and they try and and and and connect them together, but they're not really connect. So the so whenever you see a claim like that the the the trick is to think you know does the statistic actually prove the point that's being claimed. Okay. So
38:54 um so so again like and you see this in supplements all the time. Um supplements make all kinds of claims. Um but if you start digging below the surface, you actually see that many for for many supplements there's been no uh you know hardcore studies done to support the claims that are being made. Okay. There's may there may have been some study of it uh but but not enough to actually uh uh determine a a causitive
39:31 relationship. Okay. So again, when you hear these kind of things, look at them critically. You know, does the does the first part really have anything to do with the second part? Okay. And then finally, the eighth uh the eighth point um that Huff makes in his book is what's called post hawk tricks and cherrypicking. So um this is a situation where data is used selectively to support a conclusion. Okay. So so for example you you and and this is now
40:08 there are there are laws to try and prevent this but you know it's very difficult to literally outlaw this kind of thing. So you may see some kind of investment and they may say, you know, we've averaged 12% returns since 2015. 12% annual returns since 2015. That sounds really good. 12%. That's that's a good return. Okay. No matter no matter what. Okay. What they neglect to do is include
40:43 2014 when the investment lost you know 50%. So if you looked at the average from 2014 to today you know the investment might have averaged more like you know 5% or something like that. So by cherrypicking the data, the the person using the statistic is able to present a picture in line with what he wants, but not one that's accurate with what's actually going on. Okay? So the so again anytime you you
41:19 see data and especially data tied to a time period, the question should be why that time period. Now, one of the ways that um uh the SEC has tried to avoid this kind of cherrypicking is to require uh organizations promoting investments to quote investment returns for certain specific time periods. One year, 5year, 15 years. Well, that's all good and well, but one year, fiveyear,
41:55 15 years, maybe everything has gone really well with that investment for the last 15 years, but if you went 20 years back, it did terribly, right? So, again, even with the laws in place, that doesn't mean that the statistics aren't cherrypicked, at least somewhat. Now, they may be they may have been cherrypicked by the SEC in this case, which you could argue would was is even worse than the promoter cherry-picking it. But but in any event, and again, there's no law that you know, you could pass a law saying
42:31 you must report statistics in a way that's meaningful to the performance of of the investment. Okay, great. But that's of course meaningful is someone in the eye of the beholder. So, so we as as consumers need to look at all the data. So, uh you know if if a partic let's take a um uh a mutual fund okay maybe that mutual fund has been around for 30 years. So then from my perspective, the relevant time period for analyzing that
43:08 mutual fund is the is 30 years, right? That's how long it's been around. And over 30 years, there's been enough market fluctuation, okay, to give us a true picture of the market. Now, if a mutual fund has only been around for a few years, well, you could look at all of the data for the mutual fund, but that's not going to if you know, the market has been pretty has been very good for the last few years. So, the fact that we're looking at the entire history of that mutual fund tells us nothing about how it's going to perform in in a in a
43:43 potential down market, right? So uh you again we need to be intelligent consumers. We need to be intelligent uh viewers of of statistics with which we're being presented in order to protect ourselves. So to to you know to to you know sort of summarize again uh Gerald Huff's book is about developing critical thinking and it's it's it's a very by the way it's a very witty book uh just 144 pages long so you can read
44:18 it in one sitting easily. I'll put a I'll put a a a link to the book in the show notes. It will of course be a um a paid link. Um so that if you click through I get you know whatever it is 5 cents if you buy the book or something like that. Um but I I I highly recommend the book and uh just just to uh you know uh cover again um the you know the various uh um points main points of the book. So sampling bias, misleading
44:55 averages, small sample sizes, percentages and probabilities, graphical distortions, causation versus correlation, the semi-attached figure, and finally post hawk tricks and cherrypicking. These are all ways that statistics are manipulated in a way to convince us to do something that may not necessarily be in our best interest. And so we need to be skeptical consumers, skeptical investors to make sure that what we're being told actually
45:33 has validity, right? And especially when we come when we we look at wealth building when we're looking at investments like the final example I gave with the mutual funds we need to be in spite of all the regulation uh we need to be very cautious and careful uh investors uh in order to make sure that we're making the best decisions possible for ourselves and our families. So, I thank you very much uh for joining me today and uh especially my my live guests. Thanks thanks for tuning in
46:09 live. Uh and this is my last show for this week and so uh I will be back next week on Monday uh at 10:00 a.m. with Trust and Estates Weekly uh 10 a.m. Pacific time. That is Tuesday at 10:00 a.m. Pacific time is my show uh business lessons from the military and my guest is going to be Jason Hollander. He uh a Marine Corps veteran and now an M&A advisor. So looking forward to talking
46:44 to Jason about how his uh his time in the Marine Corps has informed his his practice uh working with mergers and acquisitions. And then on Wednesday at 10 a.m. next week is our uh once a month show uh economic echoes and and we're we're going to this is when we look at all kinds of statistics. So uh today's show is a is a good preparation for next week's show, people making money um at
47:20 uh at 10:00 a.m. uh Pacific time. And then finally, next week on Thursday is my every other uh Thursday show at 5:00 p. p.m. Pacific time, Jiggers's Place. We're going to have a cocktail and we're going to talk about some great books. I'm going to talk about three of my all-time favorite um authors from the 19th century. uh Jane Austin, um uh uh um George Elliot and
47:55 Thomas Hardy. Um among the books that they wrote, uh Jane Austin is North Anger Abby. Not one of her best known, probably her best known is either uh Sense and Sensibility, uh or gosh, what's her other well-known one? I tend to like her lesser known books. Um, George Elliott, author of such books as Daniel Deronda. Um, and I think he als it's actually a she. It's George Elliot, but it's that's a pseudonym for a a female author. U Mill on the Floss, I think, is also Daniel Deronda. And then Thomas Hardy,
48:32 Jude the Obscure, Tess of the Dubervilles. Um, marvelous, marvelous books. all extremely entertaining with a lot of life lessons in them. So, join me for a cocktail and a schmoo about books next Thursday at 5:00 p. p.m. Pacific time. And so now it just leaves me to wish you a wonderful rest of the day and rest of the week and to sign off as I always do. Marie darling, you are my bell. Courage at all times, my friends.