Analyzing relationships and

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A determining the significance of data
Next, determine whether the relationship is significant; when a relationship is statistically significant, the observed relationship is more likely to occur in the future. When it comes to claiming statistical significance, some people may assume that there must be a complicated formula that only experienced statisticians can calculate. In reality, statistical significance is determined using the t-test, a simple statistical test that compares two samples to allow us to estimate the correlation of the same relationships in future samples.

In this case, campaigns with pitch counts below 70 brazil number data one group and campaigns above 71 are the other group. The results below describe the percentage difference between the means of the two groups (i.e., campaigns from Q2 and Q3) to determine whether the low pitch count had the desired effect for each metric. The asterisks are statistically significant, meaning there is less than a 5 percent chance that the observed results are due to chance.



How our analysis can improve your digital PR team's efforts.
In practice, the relationships between these metrics help you establish a better standard of practice for your team’s outreach with realistic expectations and goals. Furthermore, the correlation between a specific pitch count range and all other KPIs gives you a reliable range of what values you can expect when it comes to pitch quality, timelines, and campaign performance metrics.