String immutability means that once a string is created, it cannot be altered. This feature is significant in programming languages for several reasons. Firstly, it enhances security, as immutable objects are inherently thread-safe and cannot be changed by multiple threads simultaneously, reducing the likelihood of concurrency issues. Secondly, immutability leads to simpler code, where strings can be passed around functions without the concern of them being modified unexpectedly. Lastly, it allows for string interning, where the language stores only one copy of each distinct string value, which can lead to significant memory savings when the same string is used multiple times.

The choice of data type can significantly influence the complexity of an algorithm. Algorithms are designed to process data efficiently, and the operations involved can vary widely between data types. For instance, sorting algorithms will have different complexities when sorting integers compared to strings due to the underlying comparison and swap operations. With integers, a simple numeric comparison is sufficient, but with strings, lexicographic comparison is required, which is more complex and time-consuming. Additionally, the data type determines the memory usage of the algorithm, with complex data types consuming more memory, which can affect the space complexity of the algorithm. Therefore, choosing the correct data type is essential to optimising both the time and space complexity of algorithms.

In some programming languages, strings are not considered primitive because they are composed of a sequence of characters, which means they can vary in length and are mutable. A primitive data type is typically a single value of a fixed size, which the language treats as a basic building block. Since strings can be of any length and can change, they require a more complex representation in memory and additional operations for manipulation, such as concatenation, searching, and substring extraction. Therefore, they are often implemented as objects or as a composite data type that provides the necessary functionality to manage a collection of characters.

Using a data type larger than necessary can lead to inefficient memory usage, which is critical in environments with limited memory resources. For example, if an integer variable is used to store boolean values, instead of using one bit, it may use 32 or 64 bits, which is a substantial increase in memory allocation for a simple true or false representation. This not only wastes memory space but can also affect the performance of the program, as the processor has to handle more data than necessary. In large-scale systems or applications running on devices with constrained memory capacities, such inefficiencies can lead to slower processing times and increased power consumption.

While real numbers can be used broadly to represent numerical values, they are not always the optimal choice. Real numbers, represented as floating-point in computers, come with their own set of complexities such as precision and rounding errors. These issues arise because floating-point numbers cannot accurately represent all real numbers due to their finite memory allocation. Moreover, operations on floating-point numbers are usually slower than integer operations, and the precision issues can lead to unexpected results in calculations. Hence, for tasks that do not require fractional values and where precision is paramount, integers are the preferred choice.