Sorting & Searching Algorothms Lowersixth Science Computer Science

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Quick true/false on sorting and searching concepts.

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YAKILI LMS

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Sorting & Searching Algorothms Lowersixth Science Computer Science


Sorting & Searching Algorothms Lowersixth Science Computer ScienceOnline version Quick true/false on sorting and searching concepts. by YAKILI LMS 1 Bubble sort swaps adjacent elements to bubble the largest to the end. Yes No 2 Binary search requires a sorted array. Yes No 3 Counting sort has time complexity O(n^2). Yes No 4 Selection sort moves the maximum element to the end in one pass. Yes No 5 Linear search checks each element until a match is found. Yes No 6 Algorithms like counting sort can be non-comparison sorts. Yes No 7 Shell sort generalizes insertion sort with gap sequences. Yes No 8 Shuffle of an array helps sorting faster. Yes No 9 QuickSort requires all elements to be distinct. Yes No 10 In-place sorting cannot be done for merge sort. Yes No 11 Radix sort sorts by value, not by digits. Yes No 12 Heap sort uses a binary heap to sort elements. Yes No 13 A binary search tree must be balanced. Yes No 14 Stable sorting preserves the relative order of equal elements. Yes No 15 The 'divide and conquer' paradigm cannot be used for searching. Yes No 16 Divide-and-conquer strategy can be used in sorting and searching. Yes No 17 Counting sort uses a frequency array to sort elements. Yes No 18 Counting sort can sort negative numbers without modification. Yes No 19 Shell sort has no gap sequence. Yes No 20 Divide and conquer reduces a problem into smaller subproblems. Yes No 21 Sorting arbitrary objects always requires a custom comparator. Yes No 22 Merge sort can be implemented without extra space in all cases. Yes No 23 Insertion sort is stable. Yes No 24 On unsorted data, binary search can still find an element in O(log n) time. Yes No 25 Linear search is faster than binary search on large sorted arrays. Yes No 26 A binary search tree can give O(log n) search on balanced trees. Yes No 27 QuickSort always runs in O(n) time. Yes No 28 Binary search on an array of size 1 takes more than one comparison. Yes No 29 Radix sort sorts numbers by individual digits. Yes No 30 Dividing by two is used in all search algorithms. Yes No 31 A sorted array cannot be modified by any sorting algorithm. Yes No 32 Heap sort requires a secondary array for sorting. Yes No 33 Binary search halves the search space with each comparison. Yes No 34 A balanced BST guarantees O(1) search. Yes No 35 Sorting an array in place is possible without extra memory in many algorithms. Yes No 36 Merge sort is in-place with no extra space. Yes No 37 Binary search works on unsorted data. Yes No 38 All sorts are comparison-based. Yes No 39 Time complexity of finding an element by binary search is O(log n) on sorted data. Yes No 40 QuickSort on average runs in O(n log n) time. Yes No 41 QuickSort partitioning rearranges elements around a pivot. Yes No 42 The time complexity of insertion sort in best case is O(n^2). Yes No 43 Selection sort finds the minimum element and places it at the beginning. Yes No 44 A single pivot always partitions the array into equal halves. Yes No 45 Bubble sort uses a divide-and-conquer approach. Yes No 46 Divide and conquer is only used for sorting, not for searching. Yes No 47 Stable sort cannot be implemented efficiently. Yes No 48 Insertion sort is not stable. Yes No 49 Merge sort has a worst-case time complexity of O(n log n). Yes No 50 Merge sort divides the array into two halves recursively. Yes No

Sorting & Searching Algorothms Lowersixth Science Computer Science

Yes or No

About this activity

Created by

Download the paper version to play

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Yes or No

Is it true?

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Sorting & Searching Algorothms Lowersixth Science Computer ScienceOnline version

by YAKILI LMS

Bubble sort swaps adjacent elements to bubble the largest to the end.

Binary search requires a sorted array.

Counting sort has time complexity O(n^2).

Selection sort moves the maximum element to the end in one pass.

Linear search checks each element until a match is found.

Algorithms like counting sort can be non-comparison sorts.

Shell sort generalizes insertion sort with gap sequences.

Shuffle of an array helps sorting faster.

QuickSort requires all elements to be distinct.

In-place sorting cannot be done for merge sort.

Radix sort sorts by value, not by digits.

Heap sort uses a binary heap to sort elements.

A binary search tree must be balanced.

Stable sorting preserves the relative order of equal elements.

The 'divide and conquer' paradigm cannot be used for searching.

Divide-and-conquer strategy can be used in sorting and searching.

Counting sort uses a frequency array to sort elements.

Counting sort can sort negative numbers without modification.

Shell sort has no gap sequence.

Divide and conquer reduces a problem into smaller subproblems.

Sorting arbitrary objects always requires a custom comparator.

Merge sort can be implemented without extra space in all cases.

Insertion sort is stable.

On unsorted data, binary search can still find an element in O(log n) time.

Linear search is faster than binary search on large sorted arrays.

A binary search tree can give O(log n) search on balanced trees.

QuickSort always runs in O(n) time.

Binary search on an array of size 1 takes more than one comparison.

Radix sort sorts numbers by individual digits.

Dividing by two is used in all search algorithms.

A sorted array cannot be modified by any sorting algorithm.

Heap sort requires a secondary array for sorting.

Binary search halves the search space with each comparison.

A balanced BST guarantees O(1) search.

Sorting an array in place is possible without extra memory in many algorithms.

Merge sort is in-place with no extra space.

Binary search works on unsorted data.

All sorts are comparison-based.

Time complexity of finding an element by binary search is O(log n) on sorted data.

QuickSort on average runs in O(n log n) time.

QuickSort partitioning rearranges elements around a pivot.

The time complexity of insertion sort in best case is O(n^2).

Selection sort finds the minimum element and places it at the beginning.

A single pivot always partitions the array into equal halves.

Bubble sort uses a divide-and-conquer approach.

Divide and conquer is only used for sorting, not for searching.

Stable sort cannot be implemented efficiently.

Insertion sort is not stable.

Merge sort has a worst-case time complexity of O(n log n).

Merge sort divides the array into two halves recursively.