Question

Explain how ANOVA technique avoids the problem of the inflated probability of making Type 1 error that would arise using the alternative method of comparing groups of two at a time using the t-test for independent groups

Answer 1

Answer:

• Each time you direct a t-test quite possibly you will make a Type 1 blunder. This mistake is normally 5%.
• By running two t-tests on similar information you will have expanded your possibility of "committing an error" to 10%.
• The recipe for deciding the new blunder rate for different t-tests isn't as basic as increasing 5% by the quantity of tests.
• Be that as it may, on the off chance that you are just making a couple of various examinations, the outcomes are fundamentally the same as on the off chance that you do.
• Accordingly, three t-tests would be 15% (really, 14.3%) et cetera.
• These are unsuitable mistakes.
• An ANOVA controls for these blunders so the Type 1 mistake stays at 5% and you can be more sure that any huge outcome you find isn't simply down to risk

There are three principle suspicions, recorded here:

• the reliant variable is regularly disseminated in each gathering that is being thought about in the restricted ANOVA.
• Along these lines, for instance, in the event that we were contrasting three gatherings (e.g., novice, semi-expert and expert rugby players) on their leg quality, their leg quality qualities (subordinate variable) would need to be regularly appropriated for the novice gathering of players, typically circulated for the semi-experts and ordinarily disseminated for the expert players.
• You can test for ordinariness in SPSS Statistics
• There is homogeneity of changes.
• This implies the populace fluctuations in each gathering are equivalent.
• On the off chance that you utilize SPSS Statistics, Levene's Test for Homogeneity of Variances is incorporated into the yield when you run a restricted ANOVA in SPSS Statistics
• Autonomy of perceptions.
• so finally these is the comparings of the groups.