Hello, my name is Elle, and this is Quid Pro Quo, your friendly neighborhood forensic psych podcast. You loaded my podcast, prepare to learn. The Quid Pro Quo podcast deals with crime, psychology, and related themes. Listener discretion is advised.
I have a couple of exciting new Quid Pro Quo developments I'd like to discuss towards the end of the podcast. This is the only time I plan to spend any real time on this stuff on air, and it does involve more content for you, so please indulge me a moment and stick around for the last few minutes. But for now, the question at hand. People keep using a word.
I do not think it means what they think it means. When the human mind is totally your jam, and your friends come from many walks of life, you end up chatting about some bizarre stuff over drinks. But there is one particular concept which seems to get a lot of airtime in those chats. Of course, no one ever seems to broach the subject until I'm at least a glass and a half of wine into the night, but luckily, one of my dubious superpowers is giving surprisingly solid, drunk psychology lectures.
This particular concept has a myriad of different layers and implications for the individual and society, but it has developed a bit of an odd reputation. I have had to defend it on so many levels, it's kind of become a pet topic for me. It is possible that by this point, this is sort of a cocktail party Stockholm syndrome, but I actually enjoy the heck out of talking about the concept of normality. Like many interpersonal concepts, the word normal has some complicated connotations, but an understanding of normality is critical to furthering our understanding of the world.
My hope is that by the end of this episode, you will have a new appreciation for the psychological concept of normal, and with any luck, we can salvage the core idea and move away from some of the unfortunate baggage that the term has picked up along the way. Many people think of normal as having a sort of moral implication. Normal is what is good and proper and healthy, what you're supposed to be. The Beaver Cleaver and Marilyn Munsters of the world, so to speak.
Normal is right and comfortable, neutral, well-balanced, and most of all prosocial. We hear it all the time, especially in true crime circles. He seemed so normal, her upbringing was normal. They had an outwardly normal relationship, you know the drill.
Normal is also considered to be a sort of absolute. People speak as if there is this shining, immutable, platonic ideal of normal, as concrete as the signature on a cabbage patch gets butt, or the purity stamp on a gold ring. That's usually where my cocktail party conversations come in, because somewhere along the line, someone has taken a perfectly pleasant human being and beaten them down emotionally for their lack of normality. And people naturally chafe at that, especially those observant enough to see that their accusers are typically eight exactly fresh peaches and rose petals.
I always hear, what really is normal anyway? No one is actually normal, that's a myth. This is of course both completely right and completely wrong. I'm going to ask you to put aside for the moment the idea of normal as what is objectively desirable and correct.
One question you might ask to dispel that is, normal for who? Think of a normal 12-year-old child in your community. How comparable is their experience to a child in a farming community in Poland, or a high-rise in Hong Kong, or an Appalachian mining town, or a fishing village in Peru. All of these situations have their own flavor of normal, but some of them may not be very nice or healthy at all.
Proper scientific methodology transforms normal from weighty cultural expectation to nearly an observation of frequency applied to a collection of data points. Not good or well-adjusted or expected of, but simply what is expected. It allows us to take stock of our reality and move forward making educated predictions with foreknowledge. Say for argument's sake, you were brought up thinking a baloney sandwich was a normal lunch, but your childhood friend showed up with a marshmallow fluff and sardine sandwich for lunch at least four out of five days a week for the entire time you were in elementary school together.
If they suggested swapping lunches one day, sight unseen, would you be expecting a baloney sandwich? Or would you say to them, thanks, but no thanks. As much as I love you buddy, I think the sandwich I expect to get from you is an abomination, and I want nothing of it. Observing the reality and totality of the data instead of applying your own expectation allows you to make an informed decision and in all likelihood, dodge a pretty disgusting bullet.
When you start looking at normality from a research point of view, it transforms from a prescribed cultural absolute to simply the condition which has been measured or observed enough times to be an expected outcome. Normal is the cornerstone of modern psychology and social research. It is the mechanism for raising psychology out of the much-seared-at philosophical soft science of its infancy and into the painstakingly quantified scientific reality it is currently growing into. It allows us to make predictions, alter circumstances, measure change, and recognize and adjust for new situations in a measurable and most importantly, repeatable way.
As much as we aim to make people's lives better in the long run, we can't do that until we have a giant collection of baselines for what people's lives already are. We cannot study what affects a crime rate without understanding how much crime we can already realistically expect in a given time and place. We cannot study how lifestyle changes affect mental health without knowing what kind of lives people are living before and then taking stock of their mental health after. We cannot devise ways to help people to make the best of hard situations without knowing what to expect in these situations in the first place.
So how do we determine what is expected? Well, it's certainly not by declaring what we want out of a situation or what everyone knows, duh, don't be stupid. A lot of people like to scoff at the idea of studying what everyone already knows. They think measuring things which are obvious is a waste of time and money, but if we do not know what is normal, how do we determine what exactly is weird?
It is exactly these everybody knows studies which allow us to move forward. We cannot measure change without understanding the baseline. Once we know that, scientifically it is on. The researchable world is our oyster.
Now, there is an entire field of study dedicated to how we analyze data. Statistical analysis can get so complicated people get entire degrees in the subject. Personally, it's a language I never took the time to learn. I know enough to say hello, how to ask where the bathroom is, and I know a few of the nastier curse words.
So this podcast is way not the place to learn about the delicate shades and nuances of statistical analysis. But in this age of information overload and clickbait pop science, it is worth understanding a few very basic concepts. Most of the really contentious statistics the average information minded person is going to trip over deal with what we call statistical significance. It can basically be summed up as looking at the particular result and asking, did this happen enough times that this really means something?
If you're doing a study of a medication and you have 10,000 subjects and exactly one of your subjects becomes violently allergic to shellfish during the trials, the statistical odds that the allergy has anything to do with the medication is absurdly low. But what if that was 20 of your subjects or 400 or 3000? In a study of 10,000 people, what do you consider more than random chance? Statistics helps you illustrate the point where you can reasonably say, hey, we really have something to investigate here.
Which generally translates better in a research write up or a grant proposal than, oh no really, I swear I got something good here. Please, can I have a bit more money? From the other end of it, what if a study had a very small number of subjects on the same medication? Say 10 people and one became allergic to shellfish?
If you didn't know the size of the study and just saw that 10% of the subjects had a specific reaction, that may look really critical. Statistical analysis can help you make sure you have a big enough pool of people in the first place to make sure random, completely coincidental happenstance doesn't totally hijack your data. Looking at factors like this, whenever you encounter scientific claims helps you as a consumer of scientific media to know when someone is blowing smoke up your tail feathers. A classic example of this would be those crazy side effects may include warning lists for medications.
Knowing that someone experienced dry mouths or sleep gambling or really discharged does not tell you all that much about your own likelihood to experience the same. Unless you know at base how many people they were looking at and out of that group how many people experienced the side effect? We can also look at medication research to illustrate one of the biggest problems we currently face with these critical zero points we're setting. Bringing it all back around to the beginning, we once again have to ask normal for who?
I have linked a couple of NIH articles on women in health research in the episode notes because along and short of it is that up until painfully recently, males were considered the less complicated group of people to test. So no one bothered to expand their pool to women unless the drug was four women and then there were often egregious ethical issues. Nursing was extrapolated from the males results unless you were a male of average weight you could pretty much throw your expectations about a drug's dosing and effects out the window. Likewise because college psych undergrads are a lovely captive audience for PhD research, psychology has long been criticized as being an absolutely excellent in-depth study of affluent Western liberal arts college students.
You can't consider a data set to be representative of a full population if you haven't actually looked at an even remotely representative sample of that population. Add to that the necessity to look at them in their environment. It is imperative to study and establish baselines in real situations. It is normal for a person in a high-crime area to have chronically heightened stress reactions, for a young molestation victim to act out sexually, for a chronically malnourished child to have developmental delays or for a person experiencing a family death to have prolonged feelings of grief and depression.
Because we have done the work to look at these people in their environments, we now know that all of these are expected consequences, even if they are not positive. One of the things that is super handy about the psychological research that tends to be applied to legal matters is the level of scrutiny. At some point, some lawyer is probably going to take their time to dig into the methodology behind the results being referenced, and somewhere down the line they will subject it to every kind of scrutiny imaginable in an attempt to either reinforce or discredit its use on the stand. No expert witness, like sitting on the stand, getting grilled by a lawyer about the fact that the basis of the research they are citing is flimsy.
The good piece of research will have frank discussions about what populations it can be reasonably applied to and where its limitations lie. Someone citing this research professionally in any capacity should be able to address these things. If you are looking at pop signs or psychology of any sort and you do not see a discussion of limitations, that is a solid clue that you have not gotten the whole story, and you can clearly not choose the wine in front of you. If you want to really understand a result, make sure that you can answer the question, normal for who.
If you want to weigh in on the concept of normality, or if you have other questions about true crime and forensics psych, seek me out at quid pro quo cast on Twitter, Facebook, Gmail, at the dot com, Patreon, and wherever fine internets are sold. I am currently the most active on Twitter. For your reference, the number of times the word normal appears in this podcast is 29. Now onto the exciting new things.
First, I have some people to thank because I have set up a Patreon page for quid pro quo. Patronage gets you a shout out on the podcast, and if you're doing a thing, a link to that will go in the show right up. To that end, I'd like to thank my first patrons. The very first is Scott Mann of the permaculturepodcast.com, a podcast dedicated to permaculture education, sustainability, gardening, organic food, and resiliency.
Aside from the Patreon, Scott and I have been in discussion about some neat things. Keep your ears open here for that name again. And then we have Alexander of South March Forge, Maker extraordinaire, and purveyor of tools, bubbles, and curiosities to dazzle the mind. He is available on Facebook.
Third is Kat Bunny, who is a GIRL's contributor from the Let's Play channel, Instant Replay Live on YouTube. And finally, Candy, who asked me to mention, KnittedKnockers.org, who facilitate the creation and distribution of special handmade breast prostheses for women who have undergone mastectomies or other procedures to the breast. Please see the links for these pages in the show notes. On the off chance you are not familiar, Patreon is a platform which allows people to pledge a set amount of money per month or per project to a creator of some sort.
You pledge money towards goals, and in response the creators pledge to reciprocate with any number of things which will of course vary by creator and pledge level. My first and apparently rather pressing goal is to expand my hosting capabilities. You might have noticed a bit of an extended gap since my last episode. Admittedly part of that was my own time constraints.
You rush a miracle man, you get rotten miracles. But a huge part of it was that I hit my hosting limit in one episode in February. I've been playing with new recording techniques for the last few episodes, so it took a while to even identify the problem. I accidentally uploaded the wrong version of my logo, which made the episode file twice as large as it should have been.
The fact that my margin of error is so tight that this even happened in the first place is pretty ridiculous, and so the Patreon. Once the critical stuff is handled, I can spruce the place up a bit. This is a one woman show, and while my MSP skills are impressive, they only get you so far. The Patreon exists to better this podcast, so while I may add different content there, this is my focus and I do not plan to pay wallet.
If you are not in the position to pledge, there are still so many ways I need your help. Most and foremost, take a moment to leave a review, share me out to your friends, or just check in and say hi. Let me know what you think and what you still want to know. As you may have figured out by now, I do really like talking about forensic psychology.
On that note, I have also started what I am calling a true crime plus book club. This will be mostly a mix of current and classic true crime books in the usual once a month online discussion format with the occasional bit of thematic fiction thrown in. It'd be pretty cool to have some two-way chat with you in more than 140 character bites. You can find links all over the Quid Pro Quo stuff.
Book for the Facebook group called Murder of the Month. Bits and Bobs from what I read there will undoubtedly be making their way into my episodes here, but I also plan to post informal review videos for supporters on the Patreon. March's book is The Devil in the White City, Murder, Magic and Madness at the Fair that Changed America by Eric Larson. Quid Pro Quo is produced from Stem to Stern by me, Elle Stewart, and is available on iTunes, Stitcher and Google Play.
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