Mendelian Genetics are named after the Augustinian friar Gregor Mendel. For us today, he is not just a symbol for the development of genetics as a branch of science, but he is also an interesting person about whom we can ask many questions:
Who was Gregor Mendel, and why did he become a monk and not a scientist? Why did he investigate patterns of inheritance, and why did he choose pea plants as his object of study?
How was his work perceived at the time? Was he celebrated, scorned, ignored…? What did he think about these reactions? What did his contemporaries believe about inheritance? Did Mendel know Charles Darwin and his work? Did Darwin know about Mendel?
Did Mendel propose those laws named after him today? Did he introduce the use of uppercase and lowercase letters to symbolize dominant and recessive traits? Did he invent that combination square still used in Biology classes today?
These and many other questions on Mendel and his work get answered in the comprehensive, interactive PowerPoint Show “Gregor Mendel – Forefather of Genetics”. Besides, the show contains examples and explanations for the Mendelian Laws.
The interactive PowerPoint Show “Gregor Mendel – Forefather of Genetics” is available in our Online Shop.
(Genetics – Gregor Mendel – Forefather of Genetics)
The button on the right triggers the download of a worksheet containing exercises on the Mendelian laws as well as monohybrid and dihybrid inheritance patterns.
As usual, the solutions for this worksheet are available in our Online Shop.
(Genetics - Mendialian Laws - Solution)
The results you obtain from using a Punnett square to determine the phenotypical combinations of traits which can be obtained by crossbreeding two organisms always represent statistical probabilities. The actual results from real-life crossbreeding activities can vary from these probabilites. Such deviations may be caused by random chance.
When the amount of collected data is large enough (here: when you conduct enough of these crossbreedings), such random chance variations usually balance each other out.
The overall result will therefore get closer and closer to the statistically expected results.
If deviations are normal and caused by random chance, or if the hypothesis underlying those statistical expectations is wrong and must be rejected, can be assessed using the 𝛘2 - test.
The video on the right focusses on this particular method.
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