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We send out daily e-mails to customers suggesting products at different times: 09:30, 12:00, 19:30. A customer can either click on a product or not. I want to know the following: Is there a significant difference in clicks depending on at what time an email is sent to the customer?

The hypothesis is set up as follows:

\begin{align} \mathcal{H}_N &= \textrm{There is no difference in number of clicks between time groups} \\ \mathcal{H}_A &= \textrm{There is a difference in number of clicks between time groups} \end{align}

The data set I have is the following

> summary(df)
 Click      Time      
 0:277551   0930:93799  
 1:3236     1200:93446  
            1930:93542  

Where 0=no click and 1=click. My first guess was a one-way ANOVA but then I have to make the assumption that my dependent variable Click is continous and normally distributed, which is not the case.

What would be an appropriate test for the scenario I've described? If I only had two timegroups I'd use test of two proportions as suggested here. Is there any test of 3 proportions?

EDIT 1: Data set as per Ben Bolkers suggestion. But here I have only 3 rows and not 6 as he suggests. I'm misunderstanding what he means.

enter image description here

EDIT 2: Fitting glm as dipetkov suggested gives the following result, using the raw data set in the form

Click Time
-----------
0     0930
1     0930
1     1200
0     0930
0     1930
...

Call:
glm(formula = Click ~ Time - 1, family = binomial, data = df)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.1595  -0.1595  -0.1520  -0.1450   3.0200  

Coefficients:
         Estimate Std. Error z value Pr(>|z|)    
Time0930 -4.54982    0.03210  -141.8   <2e-16 ***
Time1200 -4.45538    0.03070  -145.1   <2e-16 ***
Time1930 -4.35849    0.02927  -148.9   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 389253  on 280787  degrees of freedom
Residual deviance:  35301  on 280784  degrees of freedom
AIC: 35307

Number of Fisher Scoring iterations: 7

All the groups seem to be significant. How do I find which one of them leads to most clicks?

Collapsing the data into:

> data
        0930  1200  1930
Clicks   981  1073  1182
Total  93799 93446 93542

and performing $\chi^2$ test for independence using chisq.test(data) gives

Pearson's Chi-squared test

data:  data
X-squared = 19.07, df = 2, p-value = 7.229e-05

which tells me that there is sufficient evidence to say that there is an association between clicks and timegroup. But I still don't know which time group gives the most clicks.

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2 Answers 2

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Regression is a very flexible approach to hypothesis testing as it actually does lots more than compute p-values. Regression estimates parameters and, as a side effect, this allows us to test hypotheses about those parameters. Estimation is usually more helpful though. For example, a logistic regression will estimate time effects (as the log odds of the probability to click). So you will learn not only if the time effects are statistically different but also how much different and which time is most effective.

However, if you are interested only in testing the null hypothesis of no difference in clicks at the three times, then you can use the chi squared test for independence. Summarise your data into a 2x3 contingency table of counts that has one row for "click" or "not click" and one column for "9:30", "12:00" and "19:00". The null hypothesis of independence means that the rows/columns have the same distribution as the row/column marginals. So in effect, no difference between the distribution of clicks/no clicks at each time point.


Update after you provided a summary of your data.

You don't need the -1 (no intercept) trick to estimate the time effects, either on the logit or on the probability scale.

Fit regression with an intercept and the default treatment contrast. The first level, Time = 0930, is selected as the reference level.

library("broom")
library("tidyverse")

dat <- tribble(
  ~Time, ~Clicks, ~Emails,
  "0930",  981, 93799,
  "1200", 1073, 93446,
  "1930", 1182, 93542
)

fit <- glm(
  cbind(Clicks, Emails - Clicks) ~ Time,
  data = dat,
  family = binomial
)

However, we don't need to know the reference level. Or to understand contrasts really (though that always helps). We can easily get estimates for the time effects, on the logit or probability scale, using the emmeans package.

emmeans(fit, ~Time)  # logit scale
#>  Time emmean     SE  df asymp.LCL asymp.UCL
#>  0930  -4.55 0.0321 Inf     -4.61     -4.49
#>  1200  -4.46 0.0307 Inf     -4.52     -4.40
#>  1930  -4.36 0.0293 Inf     -4.42     -4.30
#> 
#> Results are given on the logit (not the response) scale. 
#> Confidence level used: 0.95

emmeans(fit, ~Time, type = "response")  # probability scale
#>  Time   prob       SE  df asymp.LCL asymp.UCL
#>  0930 0.0105 0.000332 Inf   0.00983    0.0111
#>  1200 0.0115 0.000349 Inf   0.01082    0.0122
#>  1930 0.0126 0.000365 Inf   0.01194    0.0134
#> 
#> Confidence level used: 0.95 
#> Intervals are back-transformed from the logit scale

Finally, note that we did all this math just to get the confidence intervals. The estimated probabilities for each levels are just Clicks / Emails otherwise.

dat %>%
  mutate(Clicks / Emails)
#> # A tibble: 3 × 4
#>   Time  Clicks Emails `Clicks/Emails`
#>   <chr>  <dbl>  <dbl>           <dbl>
#> 1 0930     981  93799          0.0105
#> 2 1200    1073  93446          0.0115
#> 3 1930    1182  93542          0.0126
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  • $\begingroup$ Well, I did a G-test on my contingency table and got that ´G = 18.75, X-squared df = 2, p-value = 8.482e-05´. This means that there is sufficient evidence to reject the null hypothesis and conclude that there IS a difference between the time groups, right? But now Its also interesting to know what timegroup gives the most clicks, for which you suggest using logistic regression? $\endgroup$
    – Parseval
    Apr 28, 2022 at 6:30
  • 1
    $\begingroup$ Yes, exactly as @Ben Bolker suggested. I just wanted to explain why it's almost always a good idea to jump into regression & estimation right away and save oneself the time to do hypothesis testing in the first place. It's never satisfactory when the conclusion is "well, the groups / conditions / interventions are not the same, statistically speaking". $\endgroup$
    – dipetkov
    Apr 28, 2022 at 6:33
  • $\begingroup$ Wow, thank you a lot for this! So the enterpretation is that there is a 1.26% click-chance for emails sent 19:30? I'm gonna have to read the documentation for the nice functions you have used. Again, many thanks! $\endgroup$
    – Parseval
    Apr 28, 2022 at 12:55
  • $\begingroup$ Yes. But we get 1.26% simply dividing Clicks by Emails. It's getting the confidence intervals that requires all that math. $\endgroup$
    – dipetkov
    Apr 28, 2022 at 13:02
  • $\begingroup$ But how does knowing the confidence interval give us information about importance of time group? I don't see why we went through all the hassle to get the CI's. How can I quantify that 19:30 gives more clicks by a certain amount? If I wanted the precentage 1.26 then I would not even have needed to fit any model. $\endgroup$
    – Parseval
    Apr 28, 2022 at 13:39
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A logistic regression with a categorical predictor variable (in R: fitted_model <- glm(Click ~ Time, data = ..., family = binomial)) should be fine (followed by anova(fitted_model)). For small data sets you might want to worry about the asymptotic nature of the likelihood ratio test (which is what's used when you say anova()), but your data set isn't small ...

You can fit the same model more efficiently if you collapse the data set into the form

clicks  total  time
...

(i.e. the data set will only have three rows, one for each time) and use glm(cbind(clicks, total - clicks) ~ time, family = binomial, data = ...) or glm(clicks/total ~ time, family = binomial, weights = total, data = ...)

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  • $\begingroup$ Hi and thanks for your answer! Could you elaborate the anova() part? I also don't understand how the data will only have 6 rows if collapsed in the format you suggested. I only have 3 different times, and for each time I can get the number of clicks. That would give me a 3x2 data frame with 'columns' time and 'clicks'? $\endgroup$
    – Parseval
    Apr 28, 2022 at 6:33
  • $\begingroup$ I don't really understand how logistic regression will help me find out which timegroup yields the most/least clicks. $\endgroup$
    – Parseval
    Apr 28, 2022 at 6:41
  • $\begingroup$ There are many, many CV posts on how to perform and interpret logistic regression with a categorical predictor. For example, here, here and here. $\endgroup$
    – dipetkov
    Apr 28, 2022 at 6:57
  • $\begingroup$ @dipetkov - Thanks, fitting the LG model is straight forward but I don't understand how Ben Bolker suggests to collapse the data set, as I see it I only get 3 rows since the outcome variable is clicks and the predictor variable is timegroup with three levels. $\endgroup$
    – Parseval
    Apr 28, 2022 at 8:41
  • $\begingroup$ my mistake ..... $\endgroup$
    – Ben Bolker
    Apr 28, 2022 at 13:30

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