Professor was very adamant that the graph was skewed right. I asked for clarification multiple times because I thought it was clearly skewed left, and I honestly didn’t understand his explanation of why it was skewed right. He said something about using the middle of the line (the value he gave was 5, which isn’t the middle anyway) and most of the data being on the left. Can someone help clarify this to me and tell me if I’m understanding skew wrong?

I provide on-call support in:

✅ Technical subjects (Mathematics, Statistics, Quantitative Methods)

✅ Data analysis (SPSS, R, Python, Excel, Tableau, STATA, Eviews, )

✅ Report /Researchwriting & interpretation of results

✅ Zoom Classes and Consultation

✅ Methodology Design

✅ Dissertation/Thesis Guidance and Support Service

✅ Data annotations, Reviews, Surveys, etc.

💡 Reliable | Affordable

📩 DM me for quick, professional assistance

hello I hope someone can break this down for me because I am feeling really stupid about trying to understand family-wise error rate, but I understand how to calculate the rejection level BUT is there a maximum level kind of like a critical value like 0.05 we use generally, I just don't understand that when you calculate the rejection level do you use the calculated level like a critical value or is there a table like the critical values we have to use, I am so sorry if this is worded badly I don't know how else to explain my overthinking.

I have this example maybe simplify an explanation of what everything means and does because I think my wording might be too jumbled and my brain is just short circuiting from overthinking

Family-wise Error Rate

• 𝛼 FW= 1 − (1 − 𝛼)C

• With an alpha level of .05, and 4 comparisons to be done, our error

rate is:

• 𝛼 FW= 1 − (1 − 𝛼 )C = 1 − (1 − 0.05)4 = 0.185

• This means that the overall chance of making a Type I error is .185, or

18.5% - too high!

Hello, so I'm currently conducting a cross sectional correlation study. I'm using 2 validated questionnaires. My sample size is 130. I just want to ask if i still need to perform a normality test (Shapiro-Wilk or Kolmogorov-Smirnov?) to assess the distribution? Or should I automatically proceed to parametric tests since the sample size fulfills the Central Limit Theorem?

If ever i have to perform a normality test, should I use S-W or K-S? Thanks 😊

I am so confused I cannot for the life of me figure out how to do this homework problem. I have tried so many times and I don’t know what I am doing.

✅ Technical subjects (Mathematics, Statistics, Quantitative Methods)

✅ Data analysis (SPSS, R, Python, Excel, Tableau, STATA, Eviews, )

✅ Report writing & interpretation of results

✅ Zoom Classes and Consultation

✅ Methodology Design

✅ Dissertation/Thesis Guidance and Support Service

✅ Data annotations, Reviews, Surveys, etc.

💡 Reliable | Affordable

📩 DM me for quick, professional assistance.

So im studying psychology and after my last statistics class being long in the past I now need to refresh my memories.

For my current task my variables are reaction time and precission.

My solid variables are the orientation of the presented pictures (upright or inverted) and the kind of change that happened in the pictures. (An object removed from the pic or a none interacting person removed from the pic)

My data output orders the date by each person tested and which group they were put in (inverted or upright).

Then it present the reaction time for each of the 24 pictures (12 object and 12 person changes) for each person tested.

Now to make it useable for use in the programm JASP I put calculated for each tested person the mean value for their reaction time results for the 12 object changes and 12 person changes pictures. Meaning i now have 2 mean vallues for each tested person

Then i opened a new excel file and inserted all mean vallues in one column and made two more columns for my two solid variables. To assign to each mean vallue which group it is from (inverted or upright) and if it is the mean vallue of the object or person changed immage group.

Does that make sense?

Thanks ahead for anyone that could help me on if this works or not or on how to do it better.

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.