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Interpreting power analyses

You are planning to run a t-test, comparing the production of 20 potato fields treated with fertilizer A versus 20 treated with fertilizer B. The production data for these fields are seen below:

Boxplots comparing potato production for twenty fields treated with each of two fertilizers

You perform a two-sample t-test, comparing the production of the fields treated with fertilizer A versus those treated with fertilizer B. You obtain the following result:

Ttest_indResult(statistic=-1.4077611103176186, pvalue=0.16733016968700729)

Using an alpha of 0.05, you classify the result of the t-test as not significant, indicating no difference between the fertilizers. You then perform a power analysis, using an effect_size of 0.6 (this is the smallest effect size that you would consider worthwhile), an alpha of 0.05 and the same sample size (20 fields each treatment) used for the t-test.

You obtain the following result for your power: 0.45603406363350196

Which of the following interpretations best fits your data?

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