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01. lib.rs on executable crate

💡 When writing binary crates, we can move the main functionalities to and use it as a library from . This pattern is quite common on executable crates.

can link with multiple files.

02. Dependency crate on Cargo.toml

When the code in the file is getting larger, we can move those into a separate library crate and use it as a dependency of the main crate. As we mentioned earlier, a dependency can be specified from a folder path, git repository or by crates.io.

a. Using folder path

Let’s see how to create a nested crate and use it as a dependency using folder path,

b. Using git repository

If you want to use a library crate on multiple projects, one way is moving crate code to a git repository and use it as a dependency when needed.

c. Using crates.io

The other way is uploading it to crates.io and use it as a dependency when needed.

🚧 First, let’s a simple “Hello world” crate it to crates.io .

💡 We have to add the description and license fields to Cargo.toml, otherwise we will get

To upload this to crates.io,

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🏗️ Okay then, Let’s see how we can use this from an another crate .

By default, Cargo looks dependencies on crates.io . So we have to add only the crate name and a version string to Cargo.toml and then run to fetch the dependencies and compile them.

💭 Crates are bit similar to the packages in some other languages. Crates compile individually. If the crate has child file modules, those files will get merged with the crate file and compile as a single unit.

💭 A crate can produce an executable/ a binary or a library. is the crate root/ entry point for a binary crate and is the entry point for a library crate.

💯 As I mentioned earlier, in here we use simplest examples to reduce the complexity of learning materials. But this is how we need to write to make the code more testable.

When importing a crate that has dashes in its name “like-this”, which is not a valid Rust identifier, it will be converted by changing the dashes to underscores, so you would write

💭 //! doc comments are used to write crate and module-level documentation. On other places we have to use /// outside of the block. And when uploading a crate to crates.io , cargo generates the documentation from these doc comments and host it on docs.rs .

//! doc comments

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A further point to note is that even if you have enough participants, you need to make sure that they’re classified correctly, and put into the right experimental group. Carrying out a study on bilinguals and monolinguals would of course be hampered if it came to light that some participants spoke one more (or less) languages than their grouping would suggest.

classified

This is particularly pertinent in studies examining different mental disorders, in which the grouping definition could be unclear. For example, studies of anxiety may need to differentiate between participants who have been diagnosed with generalized anxiety disorder, or those who suffer from panic attacks, and even if participants exhibit subclinical / prodromal symptoms.

Ensuring that the sample is well-defined, and well characterized, before beginning the study will therefore ensure that the findings are relevant to the group of interest.

Picking cherries, dredging data

While most of the selection biases occur before the data has been collected, there are several steps that occur post-hoc which are open to erroneous distortion. These steps instead to relate to how the data, rather than the sample, is selected.

Cherry-picking is undoubtedly a good way to prepare a pie, but is also the phrase given to the act of only selecting data that conforms with what the experimenter is expecting, or hoping, to see.

Cherry-picking

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, or perhaps wishful thinking on behalf of the investigator. Ultimately though, this leads to bad science either way. The investigator must remain open-minded to the contents of the data, and question how they are interpreting things. It may also help if several people (ideally independent) check the data of the study.

Similar to the above, data-dredging (also known as fishing for data, or p-hacking ) is the practice of only considering the data that is significant after the experiment, and inventing post-hoc conclusions for why that emerged. This usually arises when a large number of variables investigated, and spurious results can appear significant.

data-dredging

By taking only significant variables from a dataset, this is essentially the same as running the same experiment multiple times, and publishing the one occurrence in which significant differences were found.

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, the research will be shown to be true or false.

Trick splits

Finally, in a similar way to misclassifying the participants before the experiment, their data can be misclassified after the fact. Incorrect partitioning of data is a way of dividing, or not using, certain parts of the data based on false assumptions.

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“Should”, because these guys are not given over to a hyperbole; when, therefore, they say something like “a challenging period of market correction looms […] until demand better aligns with supply,” it is time to heed the warning, to batten down the hatches.

Even more than the usage of “looms” — though an excellent, appropriate word in the circumstances — it’s the perceived misalignment of supply and demand that is ominous: in the context of “declining consumer visit frequencies,” “rising cost pressures,” and “concerns from over-expansion,” that only realistically means one thing: closures, potentially lots of them.

There is a checklist of possible remedies that existing operators are encouraged to tick off — “targeted pricing, encouraging trading up, effective use of promotions and upselling and developing delivery” — but any restaurateur not aware of fundamentals like these is likely long out of business already. And so it’s back to more pessimism, with branded chains stealing share from the huge, slow-moving independent sector but still forecast to chug along at lower growth rates than in the past, as they cut back on planned expansions and focus on same-store sales.

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