Still Not Significant

I just couldn’t resist reblogging this post because of the wonderful list of meaningless convoluted phrases people use when they don’t get a “statistically significant” result. I particularly like:

“a robust trend toward significance”.

It’s scary to think that these were all taken from peer-reviewed scientific journals…

Probable Error

Image

What to do if your p-value is just over the arbitrary threshold for ‘significance’ of p=0.05?

You don’t need to play the significance testing game – there are better methods, like quoting the effect size with a confidence interval – but if you do, the rules are simple: the result is either significant or it isn’t.

So if your p-value remains stubbornly higher than 0.05, you should call it ‘non-significant’ and write it up as such. The problem for many authors is that this just isn’t the answer they were looking for: publishing so-called ‘negative results’ is harder than ‘positive results’.

The solution is to apply the time-honoured tactic of circumlocution to disguise the non-significant result as something more interesting. The following list is culled from peer-reviewed journal articles in which (a) the authors set themselves the threshold of 0.05 for significance, (b) failed to achieve that threshold value for…

View original post 2,779 more words

6 Responses to “Still Not Significant”

  1. Anton Garrett Says:

    Negative results can be pretty important. Remember those showing that there was no aether?

    • telescoper Says:

      Yes, they can. But they should not be dressed up as a “robust trend towards” a positive result!

      • Anton Garrett Says:

        Was the author suggesting that a process was taking place in time such that the same experiment would subsequently find significance, or just bullshitting?

      • telescoper Says:

        Difficult to say as the quotes are taken out of context, but a single value certainly represent a trend!

  2. Anton Garrett Says:

    This reminds me of the way to calculate the probability of rain as the amount of probable rain that falls divided by the sum of the improbable rain and the probable rain.

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