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Обзоры Minimal Affect

Minimal Affect — Mass Effect на минималках? Нет, это пародийная ролевая игра от разработчиков Immortal: Unchained

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    Давненько мы не встречали игровых пародий. Шведская студия Toadman Interactive решила исправить данное упущение, представив пародию на Mass Effect, а заодно и все ролевые игры в научно-фантастическом сеттинге.

    Сюжет Minimal Affect расскажет об испытаниях, выпавших на долю бравой команды « Звёздный Альянс Белоголовый Орлан» , отправившейся в своё первое приключение. Последнее быстро превращается в квест по спасению галактики. Фрагменты истории вы можете увидеть в дебютном трейлере игры:

    Внешне Minial Affect сильно напоминает современные мультики, вроде анимационного сериала от Netflix «Крайний космос». Велика вероятность, что игра располагает таким же непочтительным чувством юмора, что подарит нам множество незабываемых моментов веселья. В остальном Minimal Affect будет полноценной ролевой игрой, включающей проработанных персонажей, интересные локации, побочные задания и продуманную боевую систему.

    Релиз Minimal Affect состоится в 2021 году на «большинстве консолей» и PC. Ранее Toadman Interactive отметилась выпуском шутера Block N Load и хардкорной ролевой игрой Immortal: Unchained.

    Добавляйтесь в наш Telegram-канал по ссылке или ищите его вручную в поиске по названию gmradost. Там мы публикуем в том числе и то, что не попадает в новостную ленту. Также подписывайтесь на нас в Яндекс.Дзене, Twitter и VK. И не забывайте, что у нас появились тёмная тема и лента вместо плиток.

    Minimum Detectable Effect (MDE)

    1. What is Minimum Detectable Effect (MDE)?

    It’s a minimum improvement over the conversion rate of the existing asset (baseline conversion rate) that you want the experiment to detect.

    By setting MDE, you define the conversion rate increase sufficient for the system to declare the new asset winner. The lower MDE you set, the slighter conversion changes will be detected by the system. Basically, MDE measures the experiment sensitivity.

    Highly sensitive settings, or low MDE, come along with a big sample size. The lower MDE, the more traffic you need to detect minor changes, hence the more money you have to spend on driving that traffic.

    So, by configuring MDE you are flexible about connecting the experiment design with the costs you are ready to incur.

    2. What’s the optimal MDE?

    There’s no such thing as an ideal MDE, so SplitMetrics can’t recommend you the optimal value. This is a key custom parameter affecting your sample size and, by implication, the costs associated with the traffic. In other words, we suggest you defining MDE by yourself, taking into consideration your individual risks – money and time.

    3. How does MDE affect my sample size?

    MDE has a dramatic effect on the amount of traffic required to reach statistical significance. To know your maximum sample size, use the Evan Miller calculator for sequential A/B sampling. Make sure that your insert relative value for MDE rather than absolute.

    By setting smaller MDE , you tell the system to detect slighter conversion rate changes, which requires more traffic and possibly time. On the other hand, the larger MDE you set, the less traffic (and possibly time) is required to finish the test.

    For example, to reach the significance level of 5%, you’ll require 2,922 total conversions with MDE = 10%. with MDE = 5% the sample size grows up to 11,141 total conversions.

    Remember, however, that the sample size you see is only the maximum threshold required for a statistically significant result. Due to the nature of sequential A/B testing, the system will constantly check the difference between conversion rates of variations under testing. Once the difference is found, the test is finished and there’s no need to score the entire sample size.

    4. How to calculate my MDE?

    Although our system can count MDE for you, we strongly recommend setting it by yourself. This parameter depends on your own risks – money you’re ready to allocate for the traffic acquisition and time you can wait for the experiment to run.

    To get MDE that works for you, you have to understand:

    • Traffic acquisition costs , or the money you invest in driving the required sample size;
    • Potential revenue generated from ASO-acquired users, in other words, the money you can make from using the new asset with a higher conversion rate.

    The best possible MDE implies that the potential revenue exceeds or compensates for the traffic acquisition costs.

    MDE calculation workflow

    Step 1. Estimate the desired conversion rate lift

    Let’s say the conversion rate of your product page with the existing icon is 20% (baseline conversion rate). You assume that the new icon should have at least a 22% conversion rate for you to use it instead of the existing icon.

    So, you have to configure an experiment in such a way that it declares the winner when the conversion rate difference is at least 22% – 20% = 2%. To set that up, you have to count your estimated MDE.

    MDE is calculated as a percent of the baseline conversion rate:

    MDE = desired conversion rate lift / baseline conversion rate x 100%

    In this example, 2% of the 20% baseline conversion rate is 10% – this is your estimated MDE for the experiment.

    Step 2. Calculate your sample size

    Next step is to get your sample size, using the Evan Miller’s calculator for sequential A/B testing.

    • Sample size calculation for experiments with two variations (A+B). Here’s what you should insert in the calculator:
    1. Your estimated Minimum Detectable Effect: 10% (in this example). Important! Make sure that you use relative MDE.
    2. Insert any value in the “Baseline conversion rate” field. As we use relative MDE, the baseline conversion rate is ignored in the sample size calculation;
    3. Statistical power: 80% (default in SplitMetrics);
    4. Significance level: 5% (default in SplitMetrics).

    You will see the following:

    Control wins if: 2,922 total conversions – this is the maximum sample size per two variations (A+B) needed to finish the experiment.

    Treatment wins: 106 conversions ahead – means that the system will sequentially check the difference in conversions between variation A (control) and B, and may finish the experiment once the difference of 106 is found, even before reaching the maximum sample size.

    • Sample size calculation for experiments with multiple variations (A+B+C+…) .
    1. Here’s what you should insert in the calculator :
    1. Estimated MDE;
    2. Any value as the baseline conversion rate;
    3. Statistical power: 80%;
    4. Significance level: when more than two variations are tested, you have to apply the Sidak correction to the significance level.

    Why? Each pair of variations has its individual significance level. As the number of variations under testing grows, so does the overall significance level because those individual values accumulate. The Sidak correction balances out individual significance levels so that the overall significance level equals 5%.

    To apply the Sidak correction, use the following significant level values:

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