Significance Test Puns

When she asked Amanda and Daniel why they loved Sam more than Anthony they both got mad, and the notebook said that was the test and that they both failed. What did that mean exactly?

I'm looking at SAT test scores by state. Each state has a different score and the number of people in each state who sat the test is different. I want to calculate the statistical significance of the differences between the states. Which test is required for this?

Thanks,

Statsnoob

Steph shoots 3003/3308 on his career, best FT% ever

Nash shoots 3060/3384 on his career, 2nd best FT% ever

One sided Two proportion test gives us a p value of 0.309. Thus we cannot reject the null hypothesis that Steph’s true FT% = Nash’s (aka it IS close)

Because free throws are constant and controlled, such a statistical significance test is valid (I think)

I thought genetic testing would allow a diagnosis to finally be made But apparently uncertain significance means it can be found in people with marfan syndrome and alo the general population

This doesn't sit well with me because I get really bad chest pain There's a mitral valve prolapse , but my aorta is within normal limits

I can do all the hand signs,stretch marks in the lower area of my back, deep eye set, high arched palate, flat feet , crowded teeth

But I'm still undiagnosed And have not been given any medication to help with the pain

I just don't understand

Hi. I am having trouble understanding which type of Significance Tests I should be running.

Consider the following User Centered Tests setup:

• List of 40 items.

• For each user 15 items are sampled randomly.

• One of the sampled items is shown. For each one, two different recommendation systems show 10 recommendations each (shuffled and no info about what was produced by each, for no bias). The user then selects which recommendations he/she finds useful for the sampled item. This process is repeated for the 15 items.

• 500 useres completed the test.

My objective is to able to say which of the recommendation systems is superior, but I would like to ensure that the results are statistically significant.

I read about ANOVA but I am not really sure that is what I need. Can someone point me in the right direction? Thanks

I am trying to plot a line graph with significance brackets above each significant difference. When I used the pairwise_t_test function from rstatix to form a table with the pvalues of the relevant groups I'm trying to compare, it makes comparisons between the two groups in the order that I do not want. Like:

> PWC
# A tibble: 3 x 10
  .y.          group1 group2    n1    n2 statistic    df        p    p.adj p.adj.signif
* <chr>        <chr>  <chr>  <int> <int>     <dbl> <dbl>    <dbl>    <dbl> <chr>       
1 WeightChange Day 0  Day 1      6     6      7.37     5 0.000724 0.000724 ***         
2 WeightChange Day 1  Day 2      6     6     -7.55     5 0.000644 0.000644 ***         
3 WeightChange Day 2  Day 3      6     6      2.67     5 0.044    0.044    *  

When I want to format to be like this instead:

> PWC1
# A tibble: 3 x 10
  .y.          group1 group2    n1    n2 statistic    df        p    p.adj p.adj.signif
* <chr>        <chr>  <chr>  <int> <int>     <dbl> <dbl>    <dbl>    <dbl> <chr>       
1 WeightChange Day 3  Day 2      6     6     -2.67     5 0.044    0.044    *           
2 WeightChange Day 2  Day 1      6     6      7.55     5 0.000644 0.000644 ***         
3 WeightChange Day 1  Day 0      6     6     -7.37     5 0.000724 0.000724 ***

So far I have managed to achieve this by reordering the levels as shown. Before:

PWC <- BWCttestp %>%  
  reorder_levels("Day", c("Day 0", "Day 1", "Day 2", "Day 3")) %>%
  pairwise_t_test(WeightChange~Day, pool.sd = FALSE, 
                  p.adjust.method = "none", 
                  paired = TRUE, alternative = "two.sided",
                  comparisons = list(c("Day 1", "Day 0"),
                                     c("Day 2", "Day 1"),
                                     c("Day 3", "Day 2"))) 

Fixed:

PWC1 <- BWCttestp %>%  
  reorder_levels("Day", c("Day 3", "Day 2", "Day 1", "Day 0")) %>%
  pairwise_t_test(WeightChange~Day, pool.sd = FALSE, 
                  p.adjust.method = "none", 
                  paired = TRUE, alternative = "two.sided",
                  comparisons = list(c("Day 3", "Day 2"),
                                     c("Day 2", "Day 1"),
                                     c("Day 1", "Day 0"))) 

However, when I

Say I have a dataset for customer purchases, and I wanted to see if customers with high income (indicator of whether they earn more than 100k annually) has an effect on their purchase amount, how would I go about doing that? Would I just do a linear regression with just the income variable and use the p value to evaluate whether income is significant? Or are there better ways?

I hope I explained my question well enough, any help is much appreciated.

I am working on a project in which I am trying to determine if disturbance size has an effect on the rate of colonization. I am using the change in abundance of species as a measure of rate of colonization.

I am Trying to run a test for significance between one categorical independent variable (species name) and one continuous independent variable (plot size) and a continuous dependent variable (change in species abundance). I tried running an ancova with the following code:

ancova.amodel <- lm(species_abundance~ï..species+treatment, data=abundance)

anova(ancova.amodel)

library(effects)

library(dplyr)

Is this the correct process to test for the significance i am looking for? is there a way to see what the effect size is and if it is a positive or negative effect?

Thanks

-H

Hello

Just a stats learner here. I have some data, two treatments and a control, and the response variable. I completed an ANOVA and got a p-value of 2.54x10^-6. Then completed a Tukey comparison and got low p-values between the two treatments, and one treatment with the control.

Previously, I had done a t-test on the other treatment and control (because according to my graph they looked the closest), p-value = 0.00196. But in the Tukey comparison the p-value is 0.25.

My query is… how can the t-test show significance but it doesn’t come up significant in the Tukey comparison??

Thanks!

I did a biology undergraduate degree and often did reports where would statistically analyse our results. P value of less than 0.05 shows that the results are statistically significant. How do these tests actually know the data is significant? For example we might look at correlation and get a significant positive correlation between two variables. Given that variables can be literally anything in question, how does doing a few statistical calculations determine it is significant? I always thought there must be more nuance as the actual variables can be so many different things. It might show me a significant relationship for two sociological variables and also for two mathematical, when those variables are so different?

Hello, lovely people.

Would anyone know how I could do a statistical test of 1 independent and 1 dependent variable? This is an observational study of >500 people if that makes any difference.

Both are measurements. The independent variable is grams/litre and the dependent variable is a score between 0 and 100. The dependent variable should be smaller when the independent variable is higher (in each participant) and vise-versa.

There is 1 group/population being measured. I just want to know how I can tell if it is statistically significant (what test should I run?)

Is this a t-test? Because there is only 1 group I don't think it's anova... (could be wrong!)

Thank you so much for any help :D

Hello,

The problem I'm trying to solve is relatively simple. Drawing - I have some data, and two models. The null hypothesis H0 and the alternative hypothesis H1. H1 has two more degrees of freedom than H0.

From the data, I visually see that H1 seems to be favoured. Then I compute a χ^2 goodness-of-fit test for both (over the entire range), and I confirm that χ(H1) < χ(H0) . I want to translate this into a significance of rejecting H0.

A colleague tells me that I can compute dχ = χ(H0) - χ(H1) , i.e. the difference between them. Then, I can integrate the χ^(2) distribution with 2 d.o.f., from dχ to infinity and that gives me the p-value. I however cannot make sense of simply "taking the difference", and said colleague cannot find the theorem back. Would such method make sense to you?

If not - what is an alternative way of proceeding?

Hello,

I (22m) recently took some blood tests at the rheumatologist after i scored positive (107) on anti-dsDNA at my PD (while also having joint pain, small amounts of Petechia, dry mouth and eyes).

However, the blood tests came back all normal except for the ANA ELISA and anti-dsDNA ELISA test, while the anti-dsDNA CLIFT test came back negative. I got a letter back saying that it's unlikely that i have Lupus or other rheumatological diseases and that a positive anti-dsDNA ELISA test can come from other conditions, but it feels a bit weird to just get declared healthy while i still have the same problems as before. Is the significance between the ELISA and CLIFT test so big that you can be declared to not have Lupu or other rheumatological diseases if the CLIFT is negative?

If I do a paired two-tailed T-test (p<0.05) on two different sets of data and get p-values for two groups, is there a way to determine which sets from the group were a significant change to determine efficiency change?

For example, if there were two store groups, Walmart and Kroger, selling apples, I could get p-values for their change from week 1 to week 2. Is there a way to find out which individual Walmarts or Krogers more efficiently sold apples as determined by the p-value?

https://www.socscistatistics.com/tests/ztest/default2.aspx

I'm using this calculator to test the significance between two proportions.

For sample one, I put 0.6 for the proportion and 100 for the sample size.

For sample two, I put 0 for the proportion and 3 for the sample size.

This outputs:

>The value of z is 2.0764. The value of p is .03752. The result is significant at p < .05.

I don't see how we can be so confident with n2=3 though.

(0.6)^3=21.6%, so an independent event with a probability of 0.6 has a 21.6% chance of occurring three consecutive times, right? How is the p-value so low then?

I’m taking my first test this Saturday, so I’m quite new to the ACT.

I’m curious, is there any meaning to C01 or B02 or any of the other names of the tests?