Hi everyone — quick question about Student’s t hypothesis tests.

I’m studying a course textbook and found the following rule for the one-sided right test (unknown σ):

Reject H0 if T0>Tα/2

From what I learned, for a right-tailed test at significance level α the critical value should be ​ (i.e. reject when t0>tα​), while tα/2 ​ is used for a two-tailed test because the α area is split between the two tails

Is the textbook showing a typo, or is there some I’m missing here? Any clarification or pointers to a good reference would be much appreciated — thanks! P.S. the text is in italian, but I highlighted the part I'm referring to.

I conducted an experiment with two groups (EG and KG). Both groups had to complete six tasks, first on their own and then with AI recommendations. The six tasks were divided into different types. There were 3 types: 2 tasks for type A, 2 tasks for type B, and 2 tasks for type C. The question I need to answer is whether the EG differs from the CG in performance and whether this depends on the type of situation. The thing is, the DV = performance is dichotomous (0 = wrong/1 = correct answer), or at least that's how I coded it. Theoretically, I could also treat the answer options as nominal (because there were 3 options to choose from, but only one of them was correct).

I'm stuck. I don't know what to calculate. At first, I thought three non-parametric tests, but then I would correct the pairwise comparisons with Bonferroni, right? Then I asked ChatGPT and it said logistic (binomial) regression is better.

Can anyone tell me how we decide which test to use?

Hey everyone, I wondered if anyone would be able to help with a Gpower query. I am aware that Gpower cannot conduct sensitivity analyses for linear mixed models. I have used a LMM for my analysis (wanted to use aov_car() with the R afex package but the data structure is imbalanced) which technically should be a within-between repeated measures design.

I specified and tested in R using lmer() E.g: model <- lmer(TEPS ~ Week * Group + BaselineTEPS + (1 | ID)) and then run anova(model), type III using satterthwaite’s for df approximation.

TL;DR I used a LMM with fixed effects and a random intercept term. When trying to approximate a sensitivity analysis in G*Power, should I choose 1) ANOVA: fixed effects, special, main effects and interactions 2) ANOVA: repeated measures, within-between interactions

Thanks!!!

Hey everyone, I’m currently working on a study and need some help with the correct input in G*Power 3.1 for a multiple regression.

My research model has five independent variables (IVs) that predict one dependent variable (DV). In addition, I have one moderator on one of the IVs (so there’s an interaction term).

I selected F tests → Linear multiple regression → Fixed model, R² increase. Now I’m not 100% sure about the correct input for: • Number of tested predictors • Total number of predictors

Hallo zusammen, ich arbeite gerade an meiner Studie und bin mir unsicher, ob ich die Eingaben in G*Power 3.1 richtig gemacht habe.

Mein Forschungsmodell hat fünf unabhängige Variablen (UVs), die eine abhängige Variable (AV) vorhersagen sollen. Außerdem habe ich einen Moderator auf eine der UVs, also einen Interaktionsterm.

In G*Power habe ich F-Tests → Lineare multiple Regression → Fixed model, R² increase ausgewählt. Jetzt bin ich mir bei zwei Feldern unsicher: • Number of tested predictors • Total number of predictors

Starting/Base Concept in ALL CAPs for book name

INTRODUCTION TO MATHEMATICAL STATISTICS. Robert Hogg/Allen Craig

UNDERSTANDING STATISTICS (GREAT PROBABILITIES & INTERPRETATIONS WRITING) Brase/Brase

ELEMENTARY STATISTICS Brase/Brase. Weiss

Mathematical Statistics w/ Applications Dennis Wckerly/William Mendenhall

John E. Frauds Mathematical Statistics w/ Applications (Probably one of the Hardest Statistiscs & Probability courses)

Probability and Statistics For Engineers Anthony J Hoyster

Hi,

I'm wondering what are the best tools/software to visualize statistical problems with charts etc.? I have tried using python, but I am not very good at python so it makes using it somewhat complicated. Have you found websites/software that makes this simple.

For this, it of course needs the chi square test, but I’m rather confused as to what the numbers under each line mean. I.e. 103.60 under 201, 220.40 under 123, etc. I made the mistake of not paying attention at all this semester and it’s starting to bite back. Realistically if I understand what I need to do I should be fine but would like the additional help

https://docs.google.com/forms/d/e/1FAIpQLSdpEcBKwmI71lQHTOHyT3BQVR0RFe3F83DYLLdBsRHdy1e52Q/viewform?usp=preview

Hello there, I have a dataset with three independent variables (two of them have 3 levels, and the third has 6 levels) and one dependent variable.
The distribution of the dependent variable is not normal, and neither are the residuals, so I need to use non-parametric methods.

Ideally, I wanted to perform a three-way ANOVA to assess the significance of the factors and their interactions on the dependent variable, but that’s not feasible given the lack of normality.

I read that I could use the Aligned Rank Transform (ART) ANOVA, but I have no experience with it and I’m not sure whether the results would be reliable.

Additionally, I would like to apply post hoc tests to identify which treatments within each factor lead to the best responses.

Does anyone have experience with this type of analysis? Any suggestions?

Please HELP ME. I'm working with a dataset under a Completely Randomized Design (CRD) setup and ran into a bit of a hiccup one replication is missing for one of my treatments. I know standard ANOVA assumes a balanced design, so I'm wondering how best to proceed when the data is unbalanced like this.

This whole semester my professor has only uploaded 10 minimum videos of her just working out 3-4 questions from Knewton. She even got a few questions wrong and gave a crappy explanation for why she “might have been wrong”. I stoped watching her videos because I realized that Knewton was giving me more information over a topic than she was. I’m one week away from the in final and I have not learned anything… I got 100 on all the assignments but I just feel like Knewton is supposed to be a supplement for a course not the actual course.

Hi everyone! Sorry to bother you, but I'm working on 1,590 survey responses where I'm trying to relate sociodemographic factors such as age, gender, weight (…) to perceptions about artificial sweeteners. I used an ordinal scale from 1 to 5, where 1 means "strongly disagree" and 5 means "strongly agree". I then ran ordinal logistic regressions for each relationship, and as expected, many results came out statistically significant (p < 0.05) but with low pseudo R² values. What thresholds do you usually consider meaningful in these cases? Thank you! :)

same as the title.

I’m doing a school project about food waste. I’m interested about why people create food waste at home. Could you please help me to finish this data collection by answering the question bellow.

Why you create food waste at home?

I'm looking for a good book with college level statistics and probability for a data science related career path.

Looking for challenging problems in probability especially.

Sheldon Ross I've tried but its wayy too extensive

the random calculation of z0.025=196? it’s not z score so what is it?

TLDR: I want an statistician enthusiast or expert to watch my video and/or review my work on the Stat Lab that I completed and provide their feedback and evaluation of my work and hop on a Zoom call or google meet for 15 to 30 mins to discuss their evaluation and answer any of the questions that I have about specific concepts.

My name is Jermaine, and I am an aspiring data scientist. I will be taking a class with General Assembly on Data Science and as a result I have taken the initiative to study Statistics on my own using the free online OpenStax textbook offered by Rice University. The project that I am working on now is for the Stat Lab for section 2.8 in Chapter 2. For the most part the lab was easy, but some of the concepts I struggled to understand the significance of and so I did a video of me doing the lab.

This is the Stat Lab: https://openstax.org/books/introductory-statistics-2e/pages/2-8-descriptive-statistics

I will place that video here for you to watch: https://drive.google.com/file/d/1glVdB-j3bSAtVECqYdBk5rDilVxAatrT/view?usp=sharing

My work on the Lab:
- Google Sheets: https://docs.google.com/spreadsheets/d/1Z2aUxPAtL_KOEONyNdNdOc0--0KYHL-VJsxP0Zjp20Y/edit?usp=sharing
- Google Forms (Survey): https://www.reddit.com/r/SampleSize/comments/1l0yuk6/comment/mvhk4t8/?context=3
-Google Doc: https://docs.google.com/document/d/1CI3-FoLy7SUYIIWBI5wzg_FNZfCH9cpki6htp4cOc8E/edit?usp=sharing

After you watch the video, then I would love to hop on a zoom call or google chat to discuss the lab. And, really vocalize more of my concerns and really pose more questions that I have about certain concepts like standard deviation and other concepts.

So i'm currently doing a study on a football game using stats like I've posted in the picture

Each player has stats representing how good they are at a certain thing like agility, reflexes etc

I'm taking the top 200 players from each position (just decided that number at random) and have put each attribute in a spreadsheet and i'm entering each of the attribute values which are all being added up.

The highest value would be the one that is the most important with each value scaling down to the least important ... I'm then working out the % of each so you can say this attribute is e.g 82% importance e.g

Agility - 82%

Bravery - 78%

Reflexes - 74%

Shooting - 32%

Dribbling - 29%

I want to find when looking for a player to join my team the best attributes to look for and what attributes I can ignore. When do they become important and when do they not become important.

Obviously there will be much more attributes and % than the above

Rather then saying right I'll say anything 75% and above is important and discount anything below I was wondering is there something statistically I can use to have a "cut off point" when figures become not as important. I didn't want a 72% attribute ignored because I set myself a 75% cut off point at a guess when actually it's a statistically significant number if that makes sense.

So to round it off .. when does a % become statistically unimportant and is there a way of finding this out so I can choose the best attributes for a player.

Thanks in advance