18 Example Sentences Showcasing the Meaning of 'Misimpression'
Jenny had a misimpression about the meeting time, and she arrived an hour late.
The misimpression that all online information is accurate can lead to misinformation spreading rapidly.
Tom's misimpression that his car had enough gas led to an unexpected breakdown on the highway.
Emily had a misimpression that learning a new language would be easy, but she soon realized the challenges involved.
Greg's misimpression that he was an excellent dancer was shattered when he joined a professional dance class.
The misimpression that wearing glasses weakens eyesight is a misconception without scientific basis.
The misimpression that Shakespeare's language is archaic and difficult can be dispelled through thoughtful study and analysis.
The misimpression that more hours of study always lead to better grades overlooks the importance of effective study techniques.
The misimpression that all cultures value the same social norms can lead to misunderstandings in a multicultural society.
The misimpression that cooking complex dishes requires expensive ingredients can be dispelled with creative recipes.
The misimpression that reading fiction is a waste of time neglects the cognitive and emotional benefits of literary engagement.
The misimpression that all endangered species are large and charismatic neglects the importance of preserving biodiversity at all levels.
Despite the misimpression that creative writing is purely subjective, there are objective elements that contribute to its quality.
The misimpression that all software engineers work in isolation overlooks the collaborative nature of many development projects.
The misimpression that all allergies result in immediate symptoms can lead to delayed diagnosis of certain allergic reactions.
The misimpression that organic farming is less productive than conventional farming is debunked by sustainable agriculture practices.
The misimpression that all online reviews are genuine and unbiased can lead to misguided consumer decisions.
Despite the misimpression that artificial intelligence is infallible, it can still be prone to biases embedded in the data it's trained on.