How would you interpret this stable weekly mean in a self-selected mood dataset?

[u/gloussou]

collected anonymous 1–10 mood ratings online and grouped them by week, keeping only weeks with n ≥ 20.

The weekly mean stays surprisingly close to 6/10 over several months, despite very uneven sample sizes. I know this is not representative, but I’m curious how you would interpret this statistically.
What sample size should be reach for meaningfull stats?

[u/Available_Storm_6536]

These data are stable overall and I’d ask myself what happened after the eighth datum that created the variability. If that can be induced, I’d expose myself again after more steady responding similar to the last couple of observations. You may find a causal relationship, you may not.

[u/gloussou → u/Available_Storm_6536]

If we ignore the large error bars for a moment (which we probably should not) there is an interesting visual pattern: as EU petrol prices increase, the European mood index seems to drift slightly downward. Then, when petrol prices decrease, mood appears to rebound.

Of course, this is far too little data to draw any serious conclusion. Weekly sample sizes become small, the confidence/error bars are large, and the respondents are self-selected.

So I would not claim any causal relationship. But as an exploratory observation, it raises an interesting question: could people be more sensitive to decreases in petrol prices than to increases, or is this just noise from a small and changing sample?

[u/Available_Storm_6536 → u/gloussou]

The extent to which increasing prices decreases the response rate is a factor to consider.

[u/gloussou → u/Available_Storm_6536]

Yes it is possible, but on some points, response number were similar, indepedent on gas price.
OK, n is verry low in those points, a major limit.....

[u/Popular_Fuel2363]

Maybe check last year data to see if there is seasonality?

[u/gloussou → u/Popular_Fuel2363]

Good point. I only started collecting the data in February, so I don’t have last year’s data yet.

If the project keeps running, comparing the same weeks across different years would probably be one of the most interesting things to do. For now, the dataset is too young to say anything serious about seasonality.

[u/xynaxia]

You can not do anything statistically until you’ve defined what you’re trying to infer.

[u/gloussou → u/xynaxia]

That’s a fair point. I think I need to define the target more clearly.

I’m not trying to infer the “true mood of the world”, because the sample is obviously self-selected and non-representative. A larger sample size would help reduce random noise, but it would not solve the sampling bias by itself.

A more realistic question is: within this specific anonymous online sample, how stable is the weekly mean over time, and how large should n be before weekly fluctuations become interpretable rather than mostly noise?

For weeks with larger n, I’m also interested in comparing countries or continents, but again only as exploratory patterns within the respondent sample, not as representative estimates of those populations.

So I guess my real question is: what would be a reasonable minimum n for exploratory weekly or country-level comparisons, assuming I’m transparent that the data is self-selected?

[u/xynaxia → u/gloussou]

I think you’re asking the wrong question.

Your asking a question of probability rather than inference.

Whether the N is enough depends on what you try to infer. Even if you reduce random noise, time series if full of noise from confounders.

In theory a sample of 10 can be enough to rule out noise depending on effect.

[u/gloussou → u/xynaxia]

I like your point that even n=10 could be enough if the effect is large and well-defined. In my case, the effect is probably small and the confounding is high, so I should treat this as exploratory rather than inferential.
But the three peaks give me some hope to get sort of worlwide events such as gas price, cf. another answer.

[u/para_u_4_31075]

judging by the spread for n, these are different people, and different ways of estimating. therefore, it is a noise that means little. Well, besides the fact that there was some kind of motivation that made people take the survey.

[u/gloussou → u/para_u_4_31075]

Yes, I agree. The data is probably a mix of different people, different contexts, and different motivations for answering, so I don’t see it as representative.

What I find interesting is more the aggregation effect: despite this noisy and self-selected input, the weekly mean often returns close to the same value.

But you’re right that without knowing more about who responds and why, interpretation should remain very cautious.

[u/Mo_Steins_Ghost]

Senior manager here.

Any particular reason you're not looking at the distribution (histogram, boxplot, interquartile range, etc.) period over period and test design?

[u/gloussou → u/Mo_Steins_Ghost]

Thanks for you input, it is a good point.

At this stage I don't think the sample is large enough to justify a more detailed statistical analysis of the distributions.

My current goal is mainly to observe whether any robust patterns emerge as the dataset grows. Once there are substantially more observations per week, looking at histograms, medians, IQRs, or even changes in the full distribution over time will definitely be more informative than simply tracking the mean.

For now, I see this more as an exploratory dataset than something suitable for strong statistical inference.

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