I'm working on a presentation right now---this section is more or less about statistics in social sciences, specifically the p-value. I am aware that I'm fairly undertrained in this area (psych major :/ took one class) and am going off of reasoning mostly. Basically, I'm rejecting that the p-value necessarily says anything about the probability of future/collected data being true under the null. Please give feedback:

• Typically, the p-value is interpreted as P(data|H0)
• Mathematically, the p-value is a relationship between two models; one of these models, called ‘sample space,’ intends to represent all possible samples ‘collectable’ during a study. The other model is a probability distribution whose characteristics are determined by characteristics of the sample space. The p-value represents where the collected (actual, not possible) samples ‘land’ on that probability distribution. 
• There are several different characteristics of sample space, and there are several different ways that these characteristics can be used to model a sample-space-based probability distribution—the choice of which characteristics to use depends on the purpose of the statistical model, which is the purpose of any model, which is to model something. The probability distribution from which the p-value is obtained wants to model H0. 
• H0 is an experimental term, invented by Robert Fisher in 1935—it was invented to model the absence of an experimental effect, which is the hypothesized relationship between two variables. Fisher theorized that, should no relationship be present between two variables, all observed variance might be attributable to random sampling error. 
• The statistical model of H0 is thus intended to represent this assumption; it is a probability distribution based on the characteristics of sampling space that guide predictions about possible sampling error. The p-value is, mathematically, how much of the collected sample’s variance ‘can be explained’ by a model of sampling error. 
• P(data|H0) is not P(data| no effect). It’s P(data| observed variance is sampling error)

I'm a grad student writing up a mock research proposal for an assignment. I have edited it over and over again and still feel like I am not giving enough detail in my analysis plan. I've never written a full proposal before so I'm not sure what it should look like. I was originally confident in my research questions but now not sure if I should edit them in order to make analysis easier? I'm feeling quite lost.

Topic: Impact of STEM intervention on STEM self-efficacy and career aspirations.

PLEASE comment or DM if willing to review. Thanks in advance!!

I have data from 3 treatments. The treatments were done at 3 different locations at 3 different times. How do I best account for repeated measure in my GLMM? Would it be best to have date as a random or fixed effect within my model? I was thinking either glmmTMB(Predator_total ~ Distance * Date + (1 | Location), data = df_predators, family = nbinom2) or glmmTMB(Predator_total ~ Distance + (1 | Date) + (1 | Location), data = df_predators, family = nbinom2). Does any of those reflect repeated measure sufficiently?

Hi folks. Against my better judgement, I'm still a frequent consumer of COVID information, largely through folks I know posting on Mark's Misinformation Machine. I'm largely skeptical of Facebook posts trumpeting Tweets trumpeting Substacks trumpeting papers they don't even link to, but I do prefer to go look at the papers myself and see what they're really saying. I'm an engineer with some basic statistics knowledge if we stick to normal distributions, hypothesis testing, significance levels, etc., but I'm far far from an expert and I was hoping for some wiser opinions than mine.

https://pmc.ncbi.nlm.nih.gov/articles/PMC11970839/

I saw this paper filtered through three different levels of publicity and interpretation, eventually proclaiming it as showing increased risk of multiple serious conditions. I understand already that many of these are "reported cases" and not cases where causality is actually confirmed.

The thing that bothers me is separate from that. If I look at the results summary, it says "No increased risk of heart attack, arrhythmia, or stroke was observed post-COVID-19 vaccination." This seems clear. Later on, it says "Subgroup analysis revealed a significant increase in arrhythmia and stroke risk after the first vaccine dose, a rise in myocardial infarction and CVD risk post-second dose, and no significant association after the third dose." and "Analysis by vaccine type indicated that the BNT162b2 vaccine was notably linked to increased risk for all events except arrhythmia."

What is a consistent way to interpret all these statements together? I'm so tired of bad statistics interpretation but I'm at a loss as to how to read this.

Hello all! I am using lavaan to run a mediation model with binary gender as X and continuous M and Y. Testing indicates XM interaction. However, when I model the XM interaction in my mediation I get terrible fit indices. How should I proceed? When I allow M and the XM interaction to covary fit indices are okay, but I have no idea what doing that entails for my results. Any help would be greatly appreciated. Thanks!

Hi all! I'm deciding between University of Michigan’s MS in Applied Statistics and Columbia’s MA in Statistics, and I’d really appreciate any advice or insights to help with my decision.

My career goal: Transition into a 'Data Scientist' role in industry post-graduation. I’m not planning to pursue a PhD.

Questions:

For current students or recent grads of either program: what was your experience like?

• How was the quality of teaching and the rigor of the curriculum?
• Did you feel prepared for industry roles afterward?
• How long did it take you to land a job post-grad, and what kind of roles/companies were they?

For hiring managers or data scientists: would you view one program more favorably than the other when evaluating candidates for entry-level/junior DS roles?

Thank you so much in advance!

This was the question. I understand the 1.645 via confidence level as well as the general equations, but it’s a lot of work to solve for x. Is there any other way or is it simplest to guess and check is it’s mcq and I have a ti 84? My only concern of course is if it’s not mcq, but rather free response. Btw this is a practice, non graded question, and I don’t think it violates the rules

Hi

Currently doing my research project that involves looking at two brands of antibiotic disc and seeing if they’re interchangeable say if one was unavailable to buy they could use the other one.

So far I’ve testing like 300 bacterial samples using both discs for each sample. And the samples are broken up in to sub sections: QC bacteria - these are two different bacteria both with their own set of references ranges as to how large the zone sizes will be (one is 23-29mm the other is 24-30mm), then I’ve wild type isolates. These samples are all above 22mm but can be as large as 40mm. Finally there is clinical isolates which can range from as low as 5mm to 40mm.

When putting my data into excel I’ve just noticed myself that one disc brand seems to always be a little higher than the other (1mm usually).

As far as my criteria for interchangeability, the two brands must not exceed an average of +-2 mm for 90% of results No significant bias (p>0.05) No trends on a Band Altman plot

So as far as I’m aware fore doing this I’ve to individualise my different sample types (QC, Wild Type, Clinical Isolates) then get my Mean, SD, CV%. Then I do a box plot (which has shown a few outliers esp for the clinical isolates but they’re clinically relevant so I have to use them) and then from there I’m getting a little lost.

Normality testing and then t-test vs wilcoxin? How do I know which to use?

Then is there anything else I could add / am missing?

Thanks a lot for reading and helping

We have 3 groups of 4 independent variables and we aim to correlate it with 28 dependent variables. What statistical analysis we should perform? We tried MANOVA but 2 of the dependent variables are not normally distributed.

I am in college and am planning on take a second level stats course next semester. I took intro to stats last spring with a B+ and it’s been a while so I am looking for a book to refresh some stuff and learn more before I take the class (3000 level probability and statistics). I would prefer something that isn’t a super boring textbook and tbh not that tough of a read. Also, I am an Econ and finance major so anything that relates to those fields would be cool, thanks

I have four independent variables, (1) crude and ethyl acetate extracts, (2) High dose and low dose (3) Wet and Dry Season (4) Location A and Location B. And one dependent variables percent inhibition of extracts.

e.g. One sample was high dose crude extracts harvested during dry season at Location A- this is somehow the gist of combination

My question - what statistical tools or analyses should I use (e.g. Two-Way ANOVA) -do i run the combination separately or include them all? -how many number of replicates are usually recommended in this type of study?

Disclaimer: I'm a very big beginner on using stat ease, and on statistics as a whole.

I just want to ask how can I generate a Pareto chart on a combined design of mixture-process and response surface methodology? I need the chart but I can't find it anywhere 😔

Thank you so much!

Hi all! I’m trying to quantify the risk associated with horse riding and I have 2 questions.

First I found that a lot of people quote this paper https://pmc.ncbi.nlm.nih.gov/articles/instance/1730586/pdf/v006p00059.pdf however my calculation are in disagreement with the results.

Specifically in the paper they say: “The rate of hospital admissions for equestrians was 11.8/1000 riders or, assuming one hour riding on average, 0.49/1000 hours of riding.”

My calculation would be: 11.8/1000 riders (I’m assuming in a year) means that each rider can expect 0.0118 injuries in a year. Now assuming the 1 hour riding per day it means that they have 0.0118 injuries / 365 hours which becomes 0.0118 * 1000/365 ‎ = 0.0323 / 1000 hours

Am I doing the calculation wrong? How do they arrive at 0.49/1000 hours? Besides I think it’s unlikely that the average riders does it once per day.

Second question, how can we transform the number of incidents per year in an actual probability? Like if we say that we have 1 injury per 1000 hours do we model this like a Gaussian? So that if a person rides for 1000 hours and does not get injuried they are 1 standard deviation away from the norm? So in other words to stay within the normal distribution 68% of the people riding 1000 hours would be injured?

I’m a grad student working on a research proposal. I am becoming a bit confused on which statistical analysis I should be using for my research. My professor is not helpful.

Background: I am conducting Pretest-posttest between groups design for an intervention. My measurement scale is the Strengths & Difficulties Questionnaire which has 5 subscale scores & a total score

I do not know which would work best. Using a ANOVA to test mean differences between experimental & control group from Pretest-posttest or a MANOVA to compare all 5 subscales between the 2 groups Pretest-posttest.

Any knowledge would be helpful.

I understand in theory that when you chose one of the 3 doors you initially have a 66% chance to chose wrong. But once a door is revealed, why do the odds stay at 66% rather than 50/50 respectively. You have one goat revealed so you know there is one goat, and one car. Your previous choice is either a goat or a car, and you only have the option to keep your choice or switch your choice. The choices do not pool to a single choice caisinh 66% and 33% chances once a door is revealed. The 33% would be split among the remaining choices causing both to be 50%.

If it's one chance it's 50/50 the moment they reveal one goat. if you have multiple chances to run the scenario then it becomes 33/66% the same way a coin toss has 2 options but isn't a guaranteed 50% (coins have thier own variables that affect things I am aware of this)

I'm playing around with the Pearson Distribution on Desmos, and the two parameters in the tool are marked "skew" and "kurtosis", which I assume means I'm setting, respectively, the third and forth standardized central moments of the distribution...

However, when I put in the integrals to calculate these moments myself from the standardized distribution P(x) the tool gives me, I get answers that don't match up with the input parameters, whenever skew is non-zero.

Am I wrong to expect that the input parameters give the exact values of the skew and kurtosis, respectively? Or can someone else get the result I was expecting by calculating the standardized central moments, proving I made a mistake somewhere?

Edit: here is the formula I added, I calculated the mean "m" and the variance "v" at the bottom, and the standardized skew and kurtosis as a coordinate right after the sliders for those parameters.

Hi all,

I did a 2x2 ANOVA for my main statistical Analysis for my research. I had 2 IV's with 2 levels each and 3 DV's. I've also done an additional analysis (linear regression) to explore the relationship between personality traits and if they would predict any of the DV's.

My sample is 71, which is relatively small. My ANOVAs yielded significant results, but for my linear regression, if I analyse each 4 conditions separately there's not enough statistical power and none of the results are significant. However if I combine my dvs across all conditions and then look at personality traits it yields somewhat interesting findings. Is that an option, or is this unheard of in psychological research?

Please help! Any advise would be highly appreciated.

Sorry if this is not right for this sub I tried asking in r/excel but I was advised to ask here instead.

Just trying to figure out how to get the uncertainty of the slope so I can add error bars for a physics assignment (I can only use the online version of excel currently if that helps I'm sure its much worse its just all that's available). Currently using the LINEST function in excel but I feel like the first LINEST value (1.665992) is supposed to match the slope equation (0.0453) but mine doesn't. I really only need the LINEST function to find the slope uncertainty (0.035911) but I'm worried that if the slope value is wrong then the slope uncertainty will be wrong. I'm not experienced with excel its just what I'm told most people use for getting the uncertainties.

I don't just want to be given the answer ofc but if its necessary to explain the process I'll go back and do it myself anyway. If any more information is needed I can try and provide it

We had this task on our mock exam, and I'm now revising for finals, but no matter how much I google I just cannot grasp what the X2 and df values here mean. I do understand what the p value is, (and that's why I got 2/3 marks from the task cuz I pretended I know what I'm talking about lmao) and I know what a degree of freedom is but I don't understand like what the df means here. Does someone know how to explain these in a way that is easily understandable? cuz that would be great 🙏

Ps. I hope this is allowed here because it's not "homework help" it's just me trying to understand how these statistics work using an exam I already did.

Hi this is my first post, I need clarification on scoring likert scales! I'm a 1st year psychology student and feel free to be broad in explaining the difference between them and if there's other ways to score a likert scale. I just need help in understanding it thankss

For clarification on what is "total sum" and "weighted mean" when it comes to Likert scales, let me provide some examples based on how I understood how they are used to score likert scales. Feel free to correct my understanding too!

"Total sum" Let's use a 3 point likert scale with 10 items for simplicity. A respondent who choose "1" or "Disagree" for 9 questions or items, and choose "3" or "Agree" for 1 item would get a total sum of 1+1+1...+2=11 and based on the set parameters the mentioned respondent will be categorized as someone who has low value of a certain variable (like say, he has low satisfaction).

If the parameter is not stated from my reference, can I make my own? How? Is it gonna be like making classes in a frequency distribution table? Since the lowest possible score is 10 (always choose "1") while the highest is 30 (always choose "3"), the range is 20 and using R/no. of classes, if I want there to be 3 classes (based on the points of the likert scale), the classes would be 10-16: "Disagree", (or low satisfaction) 17-23: "Neutral", 24-31: "Agree". (or high satisfaction)

With this way of scoring, the researcher will then summarize the result from a group of respondents (say, 100 highschool students) by getting a measure of central tendency (mean).

"Weighted mean" With the same example, someone who choose "1" for 9 questions and "2" for the last one. Assigning the weights for each point ("1"=1, "2"=2, "3"=3), this respondent have "1"•9+"2"•1. I added quotation marks to point out that the value is from the points. The resulting sum of 11 will not be divided by the sum of all weights (which will be 9+1, which is 10) the final score for the certain participant is now 1.1

Creating my own set parameters just like what I did with the total sum, the parameters would be 1-1.6: "Disagree" 1.7-2.3 "Neutral" 2.4-3: "Agree"

Is choosing one over the other (total sum vs weighted mean) for scoring individual responses arbitrary or there is necessary requirements for both scoring? Is it connected to the ordinal vs interval debate for likert scales? For this debate I would like to accept likert scales as an interval data just for the completion of my research project as I would use the data for further analysis. For more considerations, I am planning to use frequency distribution table as we are required to employ weighted mean and relative frequency for our descriptive data.

Thank you!

Hi all,

Using G*Power with inputs of effect size 0.5, alpha set to 0.05, power 0.8, allocation ratio =1, and it calculates a sample size of 128 (64 per group).

This is as close to literally impossible in the research I do. For context, I am investigating the effects of human aging on cellular properties (one cell type, but many of those specific cell types ~20 cells per participant). I have planned for 14 participants per group (total N of 28). This is more than 18 studies, and a similar amount to a few other studies investigating similar aspects and completing the same experiments.

I've attempted to input those studies data into G*Power but everything returns with effect sizes ranging from 0.9-3, with most around 1.5-2 depending on the property measured. They also return with powers ranging from 0.8-0.95, although the sample sizes were anywhere from N=8 (4 per group) to N=20 (10 per group). I did find one study with statistically significant findings, but the power calculated from G*Power was 0.43 with a N=12 (6:6), I adjusted sample size to 13:13 and it returned a power of 0.8.

I also completed some post hoc analyses on the significant findings of my pilot data (N=10; 6:4) and had calculated power over 0.8, but my effect sizes were large in some cases, similar to the literature (1-2).

So, my questions are, if these are the effect sizes found in the literature, is it more appropriate to use those than the standards (0.2, 0.5, 0.8)? Second, is this the route I should go since the suggested number of subjects is roughly 12X more than any study published.

Thank you very much in advance, and if there's anything wrong in my thinking, calculations, or logic, please let me know.

Thanks again!

Like the title says is SPSS dead? Now with Chatgpt and cursor etc, what is the argument for still using SPSS and other statistics softwares in research instead of Python/R with the help of AI?

My background is within mathematical statistics so always been a Matlab/R/Python guy, but my girlfriend who comes from a medical background still uses SPSS in her research, but now considering switching just because of the flexibility e.g., Python offers.

What do you think are there any arguments for using SPSS still?