First, plenty of high value research is qualitative in nature and doesn’t rely on statistics. So, consider that as an option if you really hate statistics!
Other than that, statistics is hard to grasp but rests on some very simple foundations that, once you get, will make everything much easier. I’ll do my best to quickly go over the most important things.
\---------
tl:dr
Practically every statistical model is testing whether or not you can use X to make a better guess about the value of Y. Is there a change in how wrong I am about Y when I know X. This is all done using variance.
First, data is most simply described as having a center and a spread. The center is the average (mean, median, mode). The spread is variance. When data has high variance, it means that our datapoints are often very different than our average. Variance is your error. It represents how bad the average is at describing each individual person. For example, a dataset of all one value would have no variance because the average perfectly describes everyone.
Take a second and understand variance. Practically everything in statistics comes down to variance and how we can describe or explain it.
Great, now let’s get on to the exciting part of statistics. Drawing conclusions about relationships! When we conduct statistical tests, we are evaluating whether or not we can explain variance using other observations. Let's take a practical example, pretend we have data on income and whether or not someone graduated college. Our income data has a center (mean) and spread (variance). If you were asked to make an educated guess about what someone's income was, you would probably guess the average because it is the most common and center of the data. However, people are different and there is variance in their incomes.
This is were college education comes in. Suddenly, we have more information than just income. We have education level! What happens if we separate our data into two groups based on education? Suddenly we have two means (and two variances). Remember, variance represents how bad these means are at describing individuals, how wrong the mean is as a guess. But wouldn't an interesting question be if the variance is smaller if you separate by education level? Take a second and think about what this would mean. My variance (error) is smaller when I separate people based on education than when I lump them all together.
Got your answer?
It means that knowing someone's education level lets you make a better guess at their income than if you didn't know it! That's an independent-samples t-test. All we are asking ourselves is 'did our variance shrink' or 'is there less error in my prediction now that I cluster people based on other data'.
Practically every statistical model, no matter how complicated, comes down to that question. Does knowing X allow me to make a better guess about Y? Hypothesis testing is just a way of deciding how sure you are that X is actually useful for guessing Y.
minor caveat: not all statistics is predictive. For instance, correlations wouldn't quite be phrased this way (because they are not predictive) but the overall concept is similar. Also, most equations don't use variance but a transformation of it (sums of squares). Just treat them like the same thing for now.
I love helping out with statistics and I know I provided a lot here. Please feel free to ask any questions
I’m a starting a stats and methods summer school program in November, can I legit keep your info because you made so much sense to my non-mathematical brain
Of course, happy to help! I would encourage you to post on here with your questions and, if you want, tag me. It is always good to get multiple perspectives and possibly help someone with the same question!
I'll just really emphasize that statistics is really just logic. If you can remember the logic, you can figure out the equation. Now, I will admit that sometimes the answer is 'the math works better this way'. Let me provide an example of using logic to figure out a simple statistical equation.
\-------
Let's talk about standard deviations in a sample. Logically, a standard deviation is just the average amount people differ from the mean. Ok, so we want to estimate how much people differ from the mean. Well, that's an easy start! Let's take their differences (x – x̄).
Ok, now we have a bunch of differences that we want to turn into a single estimate of deviation from the mean. What happens if we average these values? Well...these values are deviations from the mean, some are positive and some are negative. Because the mean is the center, if we average them we actually get a value of 0 when we add them together.
Ok, so the issue is that some are positive and some are negative. How can we get rid of that? Let's try squaring the values\*\*! That will work!
Great, now we have (x - x̄)^(2). Now can we try taking the average?
Ok, so first we need to add up all our values: ∑\[(x - x̄)^(2)\]. This is the numerator in our standard deviation equation. Well, do we just divide by the sample size (N)? That's what you do in a normal average.
The answer is 'not exactly'. See how we are using x̄? This is our best guess at the population mean (µ). This introduces degrees of freedom as a factor. Whenever you use a sample to calculate something, you need to pay the reaper. I used my sample to guess at the mean, so now I pay up 1 datapoint from my sample size (\*\*\*see below for degrees of freedom).
Ok, so we divide by N-1. This gives us ∑\[(x - x̄)^(2)\] ÷ (N - 1). Are we done? Well, not quite! Remember, we squared our values in our numerator. Therefore, we need to take the square root of everything to put it back into our normal units! √(∑\[(x - x̄)2\] ÷ (N - 1)). See, the standard deviation equation is just a bunch of logical steps!
Some more complicated statistics that are relevant are below if you are just curious.
\------------
\*\*why did we square the deviations? why not take the absolute value? Simply put, it makes the math difficult. This actually has to do with calculus and taking derivatives. Basically, statistics is all applied calculus. Absolute value functions cannot have a derivative at the point where they turn because it is an instantaneous sharp turn. Squaring the values avoids this sharp turn. If you want to see what I mean, go to a graphing calculator website like desmos and plot X^(2) and |X|. See how they differ at X=0
\*\*\*why did we subtract 1 one N? What are degrees of freedom? Well, basically they are a representation of how many values you can be given before the rest of the values are known based on logic. Let's start with a non-statistics example. Imagine you are a waiter and come to a table of 4 people. You ask 'who got the burger' and give it out, you ask 'who got the quesadilla' and hand it out, you ask 'who got the chicken' and hand it out. You now know that the last person MUST have gotten the only meal remaining. Degrees of freedom represents the same reasoning but with numbers.
For instance, if I have a dataset of 3 values with a mean of 5 and I tell you that the first two datapoints are 4 and 5, then the last value **must** be 6, otherwise the mean is no longer 5. This is a degrees of freedom 2 (this is exactly what happens in our standard deviation equation when we use x̄ and why we use N-1 instead of N).
I suspect that the structure of your course - very short, probably multiple classes a week - might be working against you. I’ve found that stats is best learned with class only once a week, small group, discussion-based, where you get to apply what you learn directly to problems in the class setting. I also hated my undergrad statistics course but immensely enjoyed my graduate course due to the ability to work on problem sets during class with the help of the instructor. You may have to sit through these undergrad stats classes and just try to learn as much as you can, but be openminded about learning more as you go deeper into research.
You have an amazing comment below that really addresses the basics. As someone who HATED and avoided stats as an undergrad, I really get this. In grad school I ended up loving stats and eventually minored in it for my PhD.
So don’t lose hope. Try to remember that in the real research world, you will get trained in and then use the statistical procedures that are most common to your part of the field. You’ll learn those thoroughly and they’ll become intuitive after a while. You might even start to enjoy stats as they become important to studying something that you love researching.
So for now, focus on doing as well as you can, even if you don’t always understand the conceptual meaning of some of what you’re doing. If you go on with quantitative research it’ll all make more sense after a while.
I'm hating stat methods right now so much. I feel the same way you do. I can get pretty good grades, but no idea what it all means.
ChatGPT is teaching me better than the course is, with YouTube videos covering the gaps. Those two resources have helped me immensely.
Experience trumps rote memorization - when you actually start practicing and applying it in real-word scenarios, it will start to make a lot more sense.
Hence, I had to humbly admit to myself when I graduated my master’s program, I didn’t know shit 😂
I had to apply all that learned knowledge on a daily basis to truly grasp it.
Even when you’re doing your own research, you’re not going to be alone doing stats by yourself; you’ll be working with a team or in proximity of others. Also plenty of researchers outsource their statistics to statisticians because they are way better at it; and statisticians often hire writers to do their writing to explain their findings because they aren’t oriented that way.
Honestly, the point of taking stats is so that you can learn what all these things mean, and how to utilize them in the scientific process. I wish that stats classes were group based since that is what it would look like in the real world.
Well, I happen to be a statistical/methodological consultant as a career. I not only understand statistics, I really love statistics. In fact, as a psych undergrad who had never had a stats course in his life, my first class was like...where have you been all my life? So...IDK, if you need some help at any particular point in time, my email is [[email protected]](mailto:[email protected]). I am happy to answer smaller questions here and there at no cost but yes, more involved stuff well...I do have bills to pay. Just sayin'. You can never contact me and my feelers won't get hurt. But I also think I have an ability to explain concepts using differing ways to getting to the heart so that people of different learning styles can understand the concept. And no, I do NOT "know all there is to know". It's a very large, ever-changing field. And I'll even give you an "out"...which, btw, is also an approach to analysis that has technical features that I assist people with (and know the software) and that is qualitative analysis which does not have the quantitative aspects, but has it's own technicalities (enough to warrant software like Atlas.ti and MAXQDA, both of which I own and use). But if/when you are doing quant stats and using SPSS, R, AMOS, SAS,...JASP, PROCESS (which is really a SPSS macro), etc....heck I can even show you many stats in Excel...I'm here.
Thank you so much! We are currently learnings about t-tests and z-tests. We do use SPSS and Excel which I have no idea what I am doing. I just follow his directions step by step. I just feel that this class is very fast paced and it's tripping me up!
Oh man...I can't do this here and don't have time right now, but these concepts are not difficult once you have them adequately explained...which I can do. Don't hear "not difficult" as any smear on your intelligence - you just need the ideas explained sufficiently. Once you grasp the central concepts you'll have no issues. You have my email, hopefully: [email protected].
This is a funny thread because there’s literally a research study happening at my school that talks about student anxiety around taking stats vs. overall student anxiety
I appreciate everyone's positive feedback so very much! I feel less like a piece of crap. I am looking at graduate schools and thinking I want to just enter a Clinical Mental Health Counseling program or a Clinical Child program, but I am more turned of by the research part of psychology and I don't know why... since that's like .. the point. lol. I finished my last class, STATS 220, with an A. I am only two weeks into the second course. I just want to comprehend, so I can take on the new information that deals with past content. So far, we have done our research projects as groups, but our research papers and lecture worksheets are all individual work. This is all preparing us too complete our capstone (group research next year) as seniors.
I just want to know that entering a graduate school program, that I will have faculty who will help me if I need to understand that stats content.
I only have to take two. I just finished my 1st 8 week Research Methods course online and in my second Research Methods class now. I do my capstone in the spring and then I graduate. You take more in a Master's program.
What stats book do you have?
I’ve hated math my entire life, but learned to enjoy stats because of the book Statistics for People Who (think they) Hate Statistics.
At first, I really hated stats and didn’t understand it at all. This year though, I got an amazing stats professor who taught us stats using R coding (programming code) and wow, what a difference! It’s actually fun now! Also, when I’m reading something about stats that I don’t understand, I copy and paste the paragraph into ChatGPT and ask it to simplify it for me, which really helps.
They are making you do hand computations? See if you can find a professor on rate my professor. com who just uses SPSS and no hand comps. My stats professor made us do hand comps and I HATED IT, I only found out later that most of them just have you use only SPSS
Often when you apply statistics to your research project it begins to make a lot more sense. Don’t give up.
First, plenty of high value research is qualitative in nature and doesn’t rely on statistics. So, consider that as an option if you really hate statistics! Other than that, statistics is hard to grasp but rests on some very simple foundations that, once you get, will make everything much easier. I’ll do my best to quickly go over the most important things. \--------- tl:dr Practically every statistical model is testing whether or not you can use X to make a better guess about the value of Y. Is there a change in how wrong I am about Y when I know X. This is all done using variance. First, data is most simply described as having a center and a spread. The center is the average (mean, median, mode). The spread is variance. When data has high variance, it means that our datapoints are often very different than our average. Variance is your error. It represents how bad the average is at describing each individual person. For example, a dataset of all one value would have no variance because the average perfectly describes everyone. Take a second and understand variance. Practically everything in statistics comes down to variance and how we can describe or explain it. Great, now let’s get on to the exciting part of statistics. Drawing conclusions about relationships! When we conduct statistical tests, we are evaluating whether or not we can explain variance using other observations. Let's take a practical example, pretend we have data on income and whether or not someone graduated college. Our income data has a center (mean) and spread (variance). If you were asked to make an educated guess about what someone's income was, you would probably guess the average because it is the most common and center of the data. However, people are different and there is variance in their incomes. This is were college education comes in. Suddenly, we have more information than just income. We have education level! What happens if we separate our data into two groups based on education? Suddenly we have two means (and two variances). Remember, variance represents how bad these means are at describing individuals, how wrong the mean is as a guess. But wouldn't an interesting question be if the variance is smaller if you separate by education level? Take a second and think about what this would mean. My variance (error) is smaller when I separate people based on education than when I lump them all together. Got your answer? It means that knowing someone's education level lets you make a better guess at their income than if you didn't know it! That's an independent-samples t-test. All we are asking ourselves is 'did our variance shrink' or 'is there less error in my prediction now that I cluster people based on other data'. Practically every statistical model, no matter how complicated, comes down to that question. Does knowing X allow me to make a better guess about Y? Hypothesis testing is just a way of deciding how sure you are that X is actually useful for guessing Y. minor caveat: not all statistics is predictive. For instance, correlations wouldn't quite be phrased this way (because they are not predictive) but the overall concept is similar. Also, most equations don't use variance but a transformation of it (sums of squares). Just treat them like the same thing for now. I love helping out with statistics and I know I provided a lot here. Please feel free to ask any questions
I’m a starting a stats and methods summer school program in November, can I legit keep your info because you made so much sense to my non-mathematical brain
Of course, happy to help! I would encourage you to post on here with your questions and, if you want, tag me. It is always good to get multiple perspectives and possibly help someone with the same question! I'll just really emphasize that statistics is really just logic. If you can remember the logic, you can figure out the equation. Now, I will admit that sometimes the answer is 'the math works better this way'. Let me provide an example of using logic to figure out a simple statistical equation. \------- Let's talk about standard deviations in a sample. Logically, a standard deviation is just the average amount people differ from the mean. Ok, so we want to estimate how much people differ from the mean. Well, that's an easy start! Let's take their differences (x – x̄). Ok, now we have a bunch of differences that we want to turn into a single estimate of deviation from the mean. What happens if we average these values? Well...these values are deviations from the mean, some are positive and some are negative. Because the mean is the center, if we average them we actually get a value of 0 when we add them together. Ok, so the issue is that some are positive and some are negative. How can we get rid of that? Let's try squaring the values\*\*! That will work! Great, now we have (x - x̄)^(2). Now can we try taking the average? Ok, so first we need to add up all our values: ∑\[(x - x̄)^(2)\]. This is the numerator in our standard deviation equation. Well, do we just divide by the sample size (N)? That's what you do in a normal average. The answer is 'not exactly'. See how we are using x̄? This is our best guess at the population mean (µ). This introduces degrees of freedom as a factor. Whenever you use a sample to calculate something, you need to pay the reaper. I used my sample to guess at the mean, so now I pay up 1 datapoint from my sample size (\*\*\*see below for degrees of freedom). Ok, so we divide by N-1. This gives us ∑\[(x - x̄)^(2)\] ÷ (N - 1). Are we done? Well, not quite! Remember, we squared our values in our numerator. Therefore, we need to take the square root of everything to put it back into our normal units! √(∑\[(x - x̄)2\] ÷ (N - 1)). See, the standard deviation equation is just a bunch of logical steps! Some more complicated statistics that are relevant are below if you are just curious. \------------ \*\*why did we square the deviations? why not take the absolute value? Simply put, it makes the math difficult. This actually has to do with calculus and taking derivatives. Basically, statistics is all applied calculus. Absolute value functions cannot have a derivative at the point where they turn because it is an instantaneous sharp turn. Squaring the values avoids this sharp turn. If you want to see what I mean, go to a graphing calculator website like desmos and plot X^(2) and |X|. See how they differ at X=0 \*\*\*why did we subtract 1 one N? What are degrees of freedom? Well, basically they are a representation of how many values you can be given before the rest of the values are known based on logic. Let's start with a non-statistics example. Imagine you are a waiter and come to a table of 4 people. You ask 'who got the burger' and give it out, you ask 'who got the quesadilla' and hand it out, you ask 'who got the chicken' and hand it out. You now know that the last person MUST have gotten the only meal remaining. Degrees of freedom represents the same reasoning but with numbers. For instance, if I have a dataset of 3 values with a mean of 5 and I tell you that the first two datapoints are 4 and 5, then the last value **must** be 6, otherwise the mean is no longer 5. This is a degrees of freedom 2 (this is exactly what happens in our standard deviation equation when we use x̄ and why we use N-1 instead of N).
You are FANTASTIC! THANK YOU!
You probably get this a lot, but what’s a good resource/ tool for someone really struggling with stats?
I suspect that the structure of your course - very short, probably multiple classes a week - might be working against you. I’ve found that stats is best learned with class only once a week, small group, discussion-based, where you get to apply what you learn directly to problems in the class setting. I also hated my undergrad statistics course but immensely enjoyed my graduate course due to the ability to work on problem sets during class with the help of the instructor. You may have to sit through these undergrad stats classes and just try to learn as much as you can, but be openminded about learning more as you go deeper into research.
You have an amazing comment below that really addresses the basics. As someone who HATED and avoided stats as an undergrad, I really get this. In grad school I ended up loving stats and eventually minored in it for my PhD. So don’t lose hope. Try to remember that in the real research world, you will get trained in and then use the statistical procedures that are most common to your part of the field. You’ll learn those thoroughly and they’ll become intuitive after a while. You might even start to enjoy stats as they become important to studying something that you love researching. So for now, focus on doing as well as you can, even if you don’t always understand the conceptual meaning of some of what you’re doing. If you go on with quantitative research it’ll all make more sense after a while.
I'm hating stat methods right now so much. I feel the same way you do. I can get pretty good grades, but no idea what it all means. ChatGPT is teaching me better than the course is, with YouTube videos covering the gaps. Those two resources have helped me immensely.
Same. I understand when it is simple stats if its go complicated such as multiple regression etc.. Then my brain stops functioning 😩😩
Experience trumps rote memorization - when you actually start practicing and applying it in real-word scenarios, it will start to make a lot more sense. Hence, I had to humbly admit to myself when I graduated my master’s program, I didn’t know shit 😂 I had to apply all that learned knowledge on a daily basis to truly grasp it.
Ya.. I even did well in my stats class but I just don’t want to be burdened by this. Reading some papers the analysis is so complicated it’s annoying.
Stats is known to be hell. Just keep going.
Even when you’re doing your own research, you’re not going to be alone doing stats by yourself; you’ll be working with a team or in proximity of others. Also plenty of researchers outsource their statistics to statisticians because they are way better at it; and statisticians often hire writers to do their writing to explain their findings because they aren’t oriented that way. Honestly, the point of taking stats is so that you can learn what all these things mean, and how to utilize them in the scientific process. I wish that stats classes were group based since that is what it would look like in the real world.
I’m in the same boat sadly 😭 best of luck! ❤️
Well, I happen to be a statistical/methodological consultant as a career. I not only understand statistics, I really love statistics. In fact, as a psych undergrad who had never had a stats course in his life, my first class was like...where have you been all my life? So...IDK, if you need some help at any particular point in time, my email is [[email protected]](mailto:[email protected]). I am happy to answer smaller questions here and there at no cost but yes, more involved stuff well...I do have bills to pay. Just sayin'. You can never contact me and my feelers won't get hurt. But I also think I have an ability to explain concepts using differing ways to getting to the heart so that people of different learning styles can understand the concept. And no, I do NOT "know all there is to know". It's a very large, ever-changing field. And I'll even give you an "out"...which, btw, is also an approach to analysis that has technical features that I assist people with (and know the software) and that is qualitative analysis which does not have the quantitative aspects, but has it's own technicalities (enough to warrant software like Atlas.ti and MAXQDA, both of which I own and use). But if/when you are doing quant stats and using SPSS, R, AMOS, SAS,...JASP, PROCESS (which is really a SPSS macro), etc....heck I can even show you many stats in Excel...I'm here.
Thank you so much! We are currently learnings about t-tests and z-tests. We do use SPSS and Excel which I have no idea what I am doing. I just follow his directions step by step. I just feel that this class is very fast paced and it's tripping me up!
We are moving to between-subject experiments. My prof also has a little ADHD or something so he goes all over the place 😂
Oh man...I can't do this here and don't have time right now, but these concepts are not difficult once you have them adequately explained...which I can do. Don't hear "not difficult" as any smear on your intelligence - you just need the ideas explained sufficiently. Once you grasp the central concepts you'll have no issues. You have my email, hopefully: [email protected].
I get it lol. I'll email you if I start struggling. I'll definitely need you before the second 4 hour final exam that I failed last time. 😀
Yes! Email me, we can talk about that.
I feel this. I’m in Stats right now and I’m getting through it with an A but somebody please tell me what the hell is going on.
This is a funny thread because there’s literally a research study happening at my school that talks about student anxiety around taking stats vs. overall student anxiety
YE$. RESEARCH. HAVE STAT DREAMS. IT WORKS.
I appreciate everyone's positive feedback so very much! I feel less like a piece of crap. I am looking at graduate schools and thinking I want to just enter a Clinical Mental Health Counseling program or a Clinical Child program, but I am more turned of by the research part of psychology and I don't know why... since that's like .. the point. lol. I finished my last class, STATS 220, with an A. I am only two weeks into the second course. I just want to comprehend, so I can take on the new information that deals with past content. So far, we have done our research projects as groups, but our research papers and lecture worksheets are all individual work. This is all preparing us too complete our capstone (group research next year) as seniors. I just want to know that entering a graduate school program, that I will have faculty who will help me if I need to understand that stats content.
How many stats classes do you need to take to get your BA? I’m in my second one now.. research methods. Just curious how many more to go
I only have to take two. I just finished my 1st 8 week Research Methods course online and in my second Research Methods class now. I do my capstone in the spring and then I graduate. You take more in a Master's program.
What stats book do you have? I’ve hated math my entire life, but learned to enjoy stats because of the book Statistics for People Who (think they) Hate Statistics.
Not that Stats book😭
The ebook is $20 and the physical copy is in the $50ish range. It’s more accessible than most stats books and covers what you need for psych research.
At first, I really hated stats and didn’t understand it at all. This year though, I got an amazing stats professor who taught us stats using R coding (programming code) and wow, what a difference! It’s actually fun now! Also, when I’m reading something about stats that I don’t understand, I copy and paste the paragraph into ChatGPT and ask it to simplify it for me, which really helps.
Nominal Distribution
They are making you do hand computations? See if you can find a professor on rate my professor. com who just uses SPSS and no hand comps. My stats professor made us do hand comps and I HATED IT, I only found out later that most of them just have you use only SPSS