在试验竣事前，，，，，，，亚马逊官方会不准时为您更新试验效果
试验效果
在试验竣事之前，，，，，，，我们每隔几周都会更新一次您的试验效果。。。。。。。您可以通过点击试验控制面板中的试验名称来会见您的效果。。。。。。。请勿凭证早期效果竣事试验，，，，，，，由于这些效果可能具有误导性。。。。。。。应期待试验竣事，，，，，，，然后再决议要宣布哪一版商品形貌。。。。。。。
提醒： 试验竣事后，，，，，，，请务必在“A+ 商品形貌治理器”中重新提交获胜商品形貌，，，，，，，从而宣布该商品形貌。。。。。。。
诠释效果
我们凭证在试验时代网络的数据，，，，，，，盘算宣布每一条商品形貌可能爆发的一系列影响。。。。。。。我们会汇总针对试验中所有已注册 ASIN 的效果。。。。。。。我们提供以下几种效果：
某个版本的商品形貌可能更好。。。。。。。例如，，，，，，，若是效果显示版本 A 更好的可能性为 75%，，，，，，，则意味着在我们盘算出的可能影响中，，，，，，，宣布版本 A 有 75% 的几率会提高销量/销售额。。。。。。。
关于每一版商品形貌，，，，，，，我们会显示： 销量、销售额、转化率、每位唯一身份会见者购置的商品数目以及分派给该版本的样本量。。。。。。。转化率是指在试验中看到 A+ 商品形貌并购置了商品的唯一身份会见者所占的百分比。。。。。。。样本量是指审查了每一版商品形貌的唯一身份会见者的数目。。。。。。。每位唯一身份会见者购置的商品数目即是销量除以样本量。。。。。。。
预估年化影响。。。。。。。系统仅会为已完成的试验填充此部分。。。。。。。它显示了宣布获胜商品形貌版本在接下来的一年内可能带来的销量及销售额增添预估值。。。。。。。关于置信水平较高的获胜商品形貌，，，，，，，您会注重到大部分预估影响都较量起劲。。。。。。。关于置信水平较低的获胜商品形貌，，，，，，，您会注重到“较差案例”的影响可能是负面的。。。。。。。这是由于试验时代效果较差的商品形貌现实上仍有可能随着时间的推移效果更好一些。。。。。。。
预估年化影响
要展望一年内的影响，，，，，，，我们会盘算获胜商品形貌的日均销量增添数，，，，，，，然后乘以 365。。。。。。。这是一个估算值，，，，，，，其中没有思量季节性、价钱转变或其他影响您现实营业的因素； ；；；；；此值仅供参考，，，，，，，我们不可包管任何收益增添。。。。。。。
【可能】列显示的是我们盘算的可能效果规模的中心值 (50%)。。。。。。。【最佳案例】和【较差案例】列显示这些效果的 95% 置信区间。。。。。。。
无定性效果
试验竣事后可能会显示无定性效果，，，，，，，或说明某个版本的商品形貌优于另一个版本的置信水平较低的效果。。。。。。。不过，，，，，，，这些效果仍然很有价值。。。。。。。
以下是导致试验得出无定性效果的一些缘故原由：
您对商品形貌所做的更改幅度太小�。。。。。。�，，，，无法显著改变买家行为
流量不敷高，，，，，，，缺乏以确定置信水平较高的获胜商品形貌
您测试的两个版本的商品形貌在推动销量方面拥有相似的效果
大大都买家在做出购置决议时并不体贴您对商品形貌做的更改
在实验明确无定性效果时，，，，，，，请参考您的试验假设。。。。。。。例如，，，，，，，凭证您的更改内容，，，，，，，无定性效果会告诉您某种类型的商品形貌不值得投资，，，，，，，由于它不会影响买家行为。。。。。。�； ；；；；；蛘撸�，，，，您可以获知两种推销商品的方法有一律效果。。。。。。。您可以运行其他试验来验证您在之前测试中的发明。。。。。。。
试验要领
这些有关试验要领的说明可资助您相识我们怎样选择试验胜出书籍，，，，，，，实现项目效果； ；；；；；不过，，，，，，，试验并非强制要求。。。。。。。
试验基于小我私家买家账户。。。。。。。在试验时代，，，，，，，每个看到您的商品形貌的买家账户都被视为试验的一部分。。。。。。。买家会随机分派到某个版本的商品形貌，，，，，，，只要在试验时代识别到该买家，，，，，，，系统就会为其显示统一项商品形貌，，，，，，，不受装备类型或其他因素的影响。。。。。。。样本量不包括无法识别买家的页面的会见量。。。。。。。我们可能会从样本中自动删除某些类型的数据（如统计异常值），，，，，，，以提高效果的准确性。。。。。。。
我们使用贝叶斯剖析要领来剖析试验效果。。。。。。。这意味着我们会凭证模子和现实试验效果构建一个概率漫衍模式。。。。。。。我们会报告后验概率漫衍的平均效应值（就商品数目转变而言）以及 95% 置信区间（也称为“可信区间”），，，，，，，并在试验时代凭证从最先以来网络的所有试验数据每周更新。。。。。。�； ；；；；；袷ごχ贸头５闹眯哦仁侵讣嘎事苤谢岫陨唐废郾⑵鹁⒂跋斓男Ч嫉陌俜直取�。。。。。。
要展望一年的影响，，，，，，，我们会盘算试验时代天天赢得和失去的处置惩罚销售量的平均差值，，，，，，，然后乘以 365。。。。。。。我们凭证后验概率漫衍提供影响力 95% 置信区间。。。。。。。
亚马逊官网原文详情：
Experiment Results
We’ll update your experiment results every few weeks until it ends. You can access your results by clicking on the experiment name in the experiments dashboard. Don’t end your experiment based on early results, as those can be misleading. Instead, wait for your experiment to end before deciding what content to publish.
Tip: When your experiment ends, make sure to publish the winning content by re-submitting it in the A+ content manager.
Interpreting Results
Based on the data collected during the experiment, we calculate a range of possible impacts of publishing each piece of content. Results are aggregated for all enrolled ASINs in an experiment. We provide a few kinds of results:
Probability that one version of content is better. For example, if we say there is a 75% probability that Version A is better, that means that 75% of the possible impacts that we calculated show a likely positive units/sales lift from publishing Version A.
For each version of content, we show: Units, sales, conversion rate, units sold per unique visitor, and sample size assigned to that version. The conversion rate is the percentage of unique visitors in the experiment who saw the A+ content and made a purchase. Sample size is the count of unique visitors who saw each version of content. Units per unique visitor is units divided by sample size.
Projected one-year impact. This section is only populated for completed experiments. It shows an estimate of the possible incremental units and sales over the next year from publishing the winning version of content. For high-confidence winners, you will notice that most of the projected impacts are positive. For low-confidence winners, you will notice that the ‘worse case’ impact may be negative. This is because there is still a chance that the content that performed worse during the experiment may actually perform better over time.
Projected One-Year Impact
To project one year impact, we calculate the average daily sales increase of the winning content and multiply by 365. This is an estimate which doesn’t take into account seasonality, price changes, or other factors that would affect your business in the real world; it is provided for informational purposes only and we cannot guarantee any incremental benefits.
The Likely column shows the median (the 50th percentile) of the range of possible outcomes we calculated. The Best Case and Worse Case columns show the 95% confidence interval of those outcomes.
Inconclusive Results
An experiment can end with results that are inconclusive, or results that show a low confidence that one version of content is better than another. However, these results can still be valuable.
Here are some reasons why an experiment may have inconclusive results:
The change you made to your content was too small to significantly change customer behavior
There wasn’t enough traffic to determine the winning content with high confidence
The two versions of content you tested were similarly effective in driving sales
The change you made to your content isn’t something that most customers care about when making a purchase decision
Refer to your experiment hypothesis when trying to make sense of inconclusive results. For example, depending on what you changed, an inconclusive result can tell you that a certain type of content isn’t worth investing in because it doesn’t affect customer behavior. Or, it can tell you that two ways of merchandising your product are similarly effective. You can run additional experiments to confirm what you’ve learned from your earlier tests.
Experiment Methodology
These notes on experiment methodology may help you understand how we choose an experiment winner and project impact; however, this is not required to run an experiment.
Experiments are based on individual customer accounts. During an experiment, each customer account that sees your content is considered part of the experiment. Customers are randomly assigned to view one version of content will see that content persistently for the duration of the experiment regardless of device type or other factors, as long as the customer can be identified. Visits to your page where a customer cannot be identified are not included in the sample size. We may automatically remove certain types of data from the sample to improve the accuracy of results, such as statistical outliers.
We use a Bayesian approach to analyze experiment results. This means we construct a probability distribution based on a model as well as the actual results of the experiment. We report the mean effect size (in terms of change in units) as well as the 95% confidence interval (also known as a credible interval) of the posterior probability distribution, which is updated weekly during the experiment based on all experiment data collected since the start. The confidence of a winning treatment is the percentage of outcomes in the probability distribution that show a positive unit sales impact.
To project one year impact, we compute the average difference between the winning and losing treatment sales per day for the duration of the experiment so far and multiply by 365. We provide a 95% confidence interval for the impact which is based on the posterior probability distribution.
文章泉源：亚马逊官方网站