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Your comment will show up after approval from a moderator. Thank you so much for the work you do 1 … 3. Binomial test Z-test for 1 proportion. Chi-square goodness-of-fit test. Sign test for 1 median. Wilcoxon signed-ranks test Sign test for 2 related medians. Z-test for 2 independent proportions. Chi-square independence test. Independent samples t-test means Levene's test variances. Final Hitpoints. King tower. Princess tower 1. Princess tower 2. Watch Replay. Recent Winners — Grand Challenge. Grand Challenge.
Top Royales. No Clan. WarxUnion 2. This is a great metric to see if your products are current and what shoppers want to buy. I can say for sure that is because I haven't launched any new products or been very diligent about my marketing efforts for my shop lately. I can't even claim it is seasonal, since last year when I was trying harder the sales were there!
Conversion rate is how many people actually bought once they saw your item for sale. You get this number by dividing the number of orders by the number of visits. Again, mine is down about the same amount as my orders, which leads me to believe that there is something wrong that I need to tweak with my listings.
Maybe I need to revisit pricing or descriptions. Or I might not have enough pictures in my listing to really show how the product will help the buyer. Conversion is another place that our own efforts can really make a difference in the number of sales we get.
The buyer was there, but I need to make my products more enticing for them to push the buy button! Revenue is the last one. That happens because I am getting fewer sales on my higher priced items and more sales on my lower priced items. Now, I am not saying you only have higher prices. The Etsy algorithm favors recent sales in their search results so any sales are good sales, even if they are for lower cost items!
This is my area and one where we can make a big difference! The left side is traffic that Etsy brought to your shop and the right side is traffic I drove to my store myself. Think about that! If I hadn't been doing marketing myself my sales would be even lower and I wouldn't have the revenue I do have!
Yours may be a way higher percentage of your visits. If the value of the test statistic is more extreme than the statistic calculated from the null hypothesis, then you can infer a statistically significant relationship between the predictor and outcome variables. If the value of the test statistic is less extreme than the one calculated from the null hypothesis, then you can infer no statistically significant relationship between the predictor and outcome variables.
You can perform statistical tests on data that have been collected in a statistically valid manner — either through an experiment , or through observations made using probability sampling methods. For a statistical test to be valid , your sample size needs to be large enough to approximate the true distribution of the population being studied. If your data do not meet the assumptions of normality or homogeneity of variance, you may be able to perform a nonparametric statistical test , which allows you to make comparisons without any assumptions about the data distribution.
If your data do not meet the assumption of independence of observations, you may be able to use a test that accounts for structure in your data repeated-measures tests or tests that include blocking variables.
The types of variables you have usually determine what type of statistical test you can use. Quantitative variables represent amounts of things e.
Types of quantitative variables include:. Categorical variables represent groupings of things e. Types of categorical variables include:. Choose the test that fits the types of predictor and outcome variables you have collected if you are doing an experiment , these are the independent and dependent variables. Consult the tables below to see which test best matches your variables.
See an example. Parametric tests usually have stricter requirements than nonparametric tests, and are able to make stronger inferences from the data. They can only be conducted with data that adheres to the common assumptions of statistical tests. The most common types of parametric test include regression tests, comparison tests, and correlation tests. Regression tests look for cause-and-effect relationships. They can be used to estimate the effect of one or more continuous variables on another variable.
Comparison tests look for differences among group means. They can be used to test the effect of a categorical variable on the mean value of some other characteristic.
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